System and method for detecting rock fall

ABSTRACT

Aspects of the invention provide systems and methods for using ballast sensors to detect rock fall events in a vicinity of railway tracks or similar roadways or tracks. The ballast sensors are spaced apart from the tracks. Particular embodiments permit the use of signals from the ballast sensors to discriminate rock fall events from other types of events and to detect the hypocenter of a rock fall event.

RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.12/972,334 filed 17 Dec. 2010 entitled SYSTEM AND METHOD FOR DETECTINGROCK FALL which is a continuation in part of Patent Cooperation Treatyapplication No. PCT/CA2009/000837 filed 17 Jun. 2009, published underWO2010/003220 and entitled SYSTEM AND METHOD FOR DETECTING ROCK FALL.This application also claims the benefit of the priority of U.S.application No. 61/073,358 filed on 17 Jun. 2008 and entitled SEISMICROCK FALL DETECTION SYSTEM.

TECHNICAL FIELD

This invention relates to detection of rock fall events. Particularembodiments provide systems and methods for rock fall detection.

BACKGROUND

Rock fall events and other similar events (e.g. avalanches and washouts)which take place in a vicinity of railway tracks can damage the track,can damage passing trains and, in some cases, can derail passing trainswhich can in turn cause significant damage to the train and to peopleand/or property being transported by the train. Damaged trains can causecorresponding damage to the environment. Similar events which take placein a vicinity of other transport-ways (e.g. roadways, bridges, subwaytracks and the like) can cause similar damage.

Prior art technology for detecting rock fall in a vicinity of railwaytracks involves so called “slide fences.” Slide fences incorporatecurrent carrying wires which extend between fence posts alongside therailway track. Falling rock may strike and break one or more of thesewires, opening the corresponding circuits and preventing current flowtherethrough. This change of current flow may be detected to generate arock fall indicator. Slide fences are unreliable, because falling rockmay not strike or break a wire, but may still represent a danger to apassing train. Slide fences also tend to generate false positiveresults, for example, when the wire are broken by animals or the like.Additionally, if a slide fence triggers (i.e. a wire is broken), thenthe slide fence must be repaired (i.e. the broken wire must be replaced)and rail traffic may be delayed until the slide fence is repaired.

There is a general desire for systems and methods of rock fall detectionthat overcome or ameliorate these and/or other deficiencies with theprior art.

SUMMARY OF THE INVENTION

One aspect of the invention provides a system for detection of rock fallin a vicinity of a section of railway track. The system comprises: aplurality of ballast sensors spaced apart along the track section, eachballast sensor located in a ballast proximate to the track section butspaced apart from rails and ties associated with the track section andeach ballast sensor sensitive to acoustic energy and configured togenerate a corresponding ballast sensor signal in response to detectingacoustic energy; and a signal processing unit operatively connected toreceive the ballast sensor signals from the plurality of ballastsensors, the signal processing unit configured to detect rock fallevents in a vicinity of the track section based, at least in part, onthe ballast sensor signals.

Another aspect of the invention provides a method for detection of rockfall in a vicinity of a section of railway track. The method involves:providing a plurality of ballast sensors spaced apart along the tracksection and locating each ballast sensor in a ballast proximate to thetrack section but spaced apart from rails and ties associated with thetrack section, each ballast sensor sensitive to acoustic energy andconfigured to generate a corresponding ballast sensor signal in responseto detecting acoustic energy; receiving the ballast sensor signals fromthe plurality of ballast sensors; and processing the ballast sensorsignals to detect rock fall events in a vicinity of the track sectionbased, at least in part, on the ballast sensor signals.

Other aspects of the invention provide computer program productscomprising computer instructions which, when executed by a processor,cause the processor to carry out the methods of the invention.

Other features and aspects of specific embodiments of the invention aredescribed below.

BRIEF DESCRIPTION OF THE DRAWINGS

In drawings which depict non-limiting embodiments of the invention:

FIG. 1 is a schematic depiction of a rock fall detection systemaccording to a particular embodiment configured to detect rock fall in avicinity of a section of railway track;

FIG. 2A shows a sensor array according to a particular embodiment whichis suitable for use with the FIG. 1 rock fall detection system and whichincorporates a ballast sensor;

FIG. 2B shows a rail sensor which may be incorporated into any one ormore of sensor arrays of the FIG. 1 rock fall detection system;

FIG. 3 is a schematic illustration of a signal processing unit accordingto a particular embodiment which is suitable for use with the FIG. 1rock fall detection system;

FIG. 4A is a plot showing digitized and time stamped sensor data typicalfor a small rock fall event obtained at the FIG. 3 data processor for anumber of sensors;

FIG. 4B is a plot showing digitized and time-stamped sensor data typicalfor a passing train obtained at the FIG. 3 data processor for a numberof sensors;

FIG. 4C is a plot showing digitized and time-stamped sensor data typicalfor a passing highrail vehicle obtained at the FIG. 3 data processor fora number of sensors;

FIG. 4D is a plot showing digitized and time-stamped sensor data typicalfor the activation of an on-sited power generator obtained at the FIG. 3data processor for a number of sensors;

FIG. 4E is a plot showing digitized and time-stamped sensor data typicalfor a significant rock fall event obtained at the FIG. 3 data processorfor a number of sensors;

FIG. 5 schematically illustrates a number of processing parameters whichmay be determined from the sensor data;

FIGS. 6A and 6B respectively depict typical digitized, time-stampedsensor data for a rock fall event and the corresponding STA/LTA ratio;

FIGS. 6C and 6D respectively show a 0.4 second segment of time stamped,digital sensor data and its corresponding FFT associated with a typicalrock fall event;

FIGS. 6E and 6F respectively show a 0.4 second segment of time stamped,digital sensor data and its corresponding FFT associated with a typicaltrain event;

FIGS. 6G and 6H respectively show a 1.0 second segment of time stamped,digital sensor data and its corresponding FFT associated with a typicalhighrail vehicle event;

FIGS. 6I and 6J respectively show an 11 second segment of time stamped,digitized sensor data and its corresponding FFT associated with theoperation of a generator in a vicinity of the FIG. 1 track section;

FIG. 7A schematically depicts a method for event detection methodaccording to a particular embodiment;

FIG. 7B schematically depicts a method for post event processing whichmay be performed as a part of the FIG. 7A event detection methodaccording to a particular embodiment;

FIG. 7C schematically depicts a method for estimating a location of arock fall event which may be performed as a part of the FIG. 7A eventdetection method according to a particular embodiment;

FIG. 7D schematically depicts a method for taking appropriate action inrespect of a rock fall event which may be performed as a part of theFIG. 7A event detection method according to a particular embodiment;

FIG. 8 is a schematic depiction of the triggered state of a number ofsensors in the FIG. 1 rock fall detection system in response to apassing train;

FIG. 9A shows a typical response of a number of sensors of the FIG. 1rock fall detection system to a rock fall event;

FIG. 9B is a schematic depiction of the triggered state of the FIG. 9Asensors;

FIG. 10 schematically illustrates a method for deployment of the FIG. 1system according to an example embodiment;

FIG. 11 schematically illustrates a method for discriminating a seriesof events that may be caused by a human or other animal according to aparticular embodiment;

FIG. 12A schematically depicts a typical signal associated with thepassage of a typical train car over a magnetic wheel detector;

FIG. 12B schematically depicts a typical signal associated with thepassage of a typical multi-car train over a magnetic wheel detector;

FIG. 12C schematically depicts the extraction of temporal differencesbetween corresponding features of the signals associated with a pair ofwheel detectors; and

FIG. 13 exhibits a typical cross-correlation waveform associated withsignals from a pair of spaced apart ballast sensors when a train ismoving (or has moved) over the FIG. 1 track section at a relativelyconstant speed.

DETAILED DESCRIPTION

Throughout the following description, specific details are set forth inorder to provide a more thorough understanding of the invention.However, the invention may be practiced without these particulars. Inother instances, well known elements have not been shown or described indetail to avoid unnecessarily obscuring the invention. Accordingly, thespecification and drawings are to be regarded in an illustrative, ratherthan a restrictive, sense.

Aspects of the invention provide systems and methods for using ballastsensors to detect rock fall events in a vicinity of railway tracks orsimilar tracks. The ballast sensors are spaced apart from the tracks.Particular embodiments permit the use of signals from the ballastsensors to discriminate rock fall events from other types of events andto detect the hypocenter of a rock fall event.

FIG. 1 is a schematic depiction of a rock fall detection system 10according to a particular embodiment configured to detect rock fall in avicinity of a section of railway track 12. Track section 12 maytypically be located in a sloped region which may present a risk of rockfall from the up slope 14 toward the downslope 16. This is notnecessary. Track section 12 may be located in a valley and may haveupward slopes on both sides thereof. In some embodiments, the length oftrack section 12 may be in a range of 100 m-5 km. In other embodiments,track section 12 may have other lengths. To facilitate this description,a number of direction conventions are used. As shown by the schematicaxes shown in FIG. 1, the z direction refers to the vertical direction(i.e. the direction of gravity), the y direction is oriented along tracksection 12 and the x direction refers to the direction that crossestrack section 12.

Rock fall system 10 comprises a plurality of sensor arrays 18 disposedalong track section 12. As discussed in more detail below, sensor arrays18 comprise one or more sensors for detecting acoustic and/orvibrational energy. In the illustrated embodiment, there are n sensorarrays 18 corresponding to track section 12. In general, the number nmay be any suitable number that provides the functionality describedbelow and may depend on the geotechnical characteristics of thesubstrate in a vicinity of track section 12.

Sensor arrays 18 are spaced apart from one another by distances 20 iny-direction. In some embodiments, distances 20 are in a range of 5-100m. In other embodiments, this range is 10-50 m. In still otherembodiments, this range is 10-30 m. Distances 20 may be based on anumber of factors, including, by way of non-limiting example:characteristics of sensors used in sensor arrays 18 (e.g. types ofsensors, signal to noise ratio, etc.), geotechnical characteristics(e.g. quality factor of geologic substrate), performance requirements(e.g. magnitude of rock fall which it is desired for system 10 todetect) and/or other factors (e.g. local weather patterns, local naturaland/or man-made sources of noise). Distances 20 may be uniform withinsystem 10, but this is not necessary. In general, distances 20 maydiffer between each adjacent pair of sensor arrays 18.

Each of sensor arrays 18 generates one or more corresponding sensorsignals 22. In the illustrated embodiment sensor signals 22 are analogsignals, but this is not necessary. In some embodiments, sensor arrays18 may output digital sensor signals. Sensor signals 22 are transmittedalong transmission lines 24 to central signal processing unit 26.Transmission lines, 24 may run through protective conduits (not shown inFIG. 1), such as pipes made of suitable metals, plastics, fiber or thelike. Transmission lines 24 may be electrically shielded to preventelectrical interference from external sources and/or to preventcross-talk between signals 22. The schematic illustration of FIG. 1shows a single signal 22 and a single transmission line 24 for eachsensor array 18. This is not necessary. In general, sensor arrays 18 maycomprise multiple sensors that generate a corresponding plurality ofsignals 22 which in turn may be transmitted to signal processing unit 26on a corresponding plurality of transmission lines 24. It will beappreciated by those skilled in the art that signals 22 from sensorarrays 18 may be multiplexed on transmission lines 24 if desired.

In the illustrated embodiment, system 10 comprises one or more optionalimage capturing devices 34. Image capturing devices 34 may compriseclosed circuit television cameras, for example. In some embodiments,image capturing devices 34 capture digital images and/or digital video.Image capturing devices 34 may be controlled by signal processing unit26 using signals 38 which are delivered to image capturing devices alongtransmission lines 40. Image data 36 captured by image capturing devices34 may be transmitted to signal processing unit 26 along the sametransmission lines 40. Transmission lines 40 may represent more than oneactual line. In some embodiments, transmission lines 40 are not requiredand camera control signals 38 may be wirelessly transmitted from signalprocessor unit 26 to image capturing devices 34 and image data 36 may bewirelessly transmitted from image capturing devices 34 back to signalprocessing unit 26.

Signal processing unit 26 may be housed in a suitably protectiveenclosure (not shown)—e.g. a small building or the like. At signalprocessing unit 26, sensor signals 22 are digitized and processed todetect rock fall events. Processing signals 22 to detect rock fallevents, which is described in more detail below, may involvediscriminating rock fall events from other events. By way ofnon-limiting example, such other events may include passing trains,passing highrail vehicles (e.g. trucks that travel on track section 12),other natural noise sources (e.g. waterfalls, falling trees or animals)and/or other man-made noise sources (e.g. power generators orpedestrians).

System 10 may optionally include a network connection 28 to a remoteworkstation 30. Network connection 28 may be a wire network connection,a wireless network connection and/or a fiber optic network connection,for example. In some embodiments, remote workstation 30 may be connectedto system 10 via network connection 28 to perform a number of functions,which may include (by way of non-limiting example): monitoring thestatus of system 10, logging or storing data captured by system 10,recalibrating or reconfiguring system 10, updating software used bysystem 10 or the like. In some embodiments, some or all of the datacaptured by sensor arrays 18 may be transmitted via network connection28 to remote workstation 30 and such data may be processed at the remoteworkstation 30 to detect rock fall events in a similar manner that rockfall events are detected by signal processing unit 26, as described inmore detail below.

System 10 may be a modular part of a greater system (not shown) whichincorporates other systems 32 similar to system 10. For example, signalprocessing unit 26 may be optionally linked (via network connection 28or via some other network connection) to similar signal processing unitsfor other systems 32 similar to system 10.

In the illustrated embodiment of FIG. 1, sensor arrays 18 are located onuphill side 14 of track section 12. This is not necessary. In someembodiments, sensor arrays 18 may be additionally or alternativelylocated on downhill side 16 of track section 12. In some embodiments, asingle sensor array 18 may comprise a plurality of acoustic orvibrational energy sensors, some of which may be located on uphill side14 and some of which may be located on downhill side 16.

Sensor arrays 18 may each comprise one or more acoustic energy sensors.By way of non-limiting example, suitable acoustic energy sensors mayinclude: electromagnetic induction based sensors (which may be referredto as geophones), accelerometers, piezoelectric sensors, electroactivepolymer based sensors, optical sensors, capacitive sensors,micromachined sensors or the like. As is known in the art, some acousticenergy sensors may be directional—e.g. some acoustic sensors may haveone or more axes on which they are more sensitive to acoustic energy. Insome embodiments, the output of these acoustic energy sensors may begenerally correlated with (e.g. proportional to) the sensed acousticenergy. In other embodiments, the output of these acoustic energysensors may be generally correlated with (e.g. proportional to) otherparameters, such as displacement, velocity or acceleration of a sensorcomponent.

FIG. 2A illustrates a sensor array 18 according to a particularembodiment which is suitable for use with rock fall detection system 10.In the FIG. 2A embodiment, sensor array 18 comprises a single,uni-axial, electromagnetic induction-type sensor 50 which is located onuphill side 14 of track section 12. Sensor 50 is located in the ballast52 which supports track section 12 and is spaced apart from tracksection 12—i.e. sensor 50 is not in direct contact with tracks 54 orties 56. In this description, this type of sensor 50 (which is locatedat least in part in ballast 52 of track section 12 and is spaced apartfrom track section 12) may be referred to as a ballast sensor. Sensor 50may be encased in a protective housing 58, which (in the illustratedembodiment) comprises a grout-filled enclosure which may be made from asuitable material such as suitable plastic, fiber, steel or the like.

Protective housing 58 (and sensor 50) may be located in a trench 60which is excavated in ballast 52 alongside track section 12. In theillustrated embodiment, a region 62 surrounding housing 58 is filledwith compacted sand, which may improve acoustic conduction and/orprotect sensor 50 and transmission line 24 from sharp rocks which may bepresent in ballast 52, and a remaining region 64 of trench 60 isback-filled with ballast 52. In the illustrated embodiment sensor 50 iscoupled to an anchoring stake 66 which may be driven into the substratebelow ballast 52 and/or below sand-filled region 62. Stake 66 may besituated, shaped and/or otherwise configured to provide good acousticcoupling to the geologic substrate in a region of track section 12.

As mentioned above, sensor 50 of the FIG. 2A embodiment is a uni-axialsensor. The sensitivity axis of sensor 50 is the z axis and sensor 50generates a single corresponding signal 22. In one particularembodiment, signal 22 is generally correlated with (e.g. proportionalto) a sensed velocity of a component of sensor 50. However, as discussedabove, in other embodiments, signal 22 may be generally correlated with(e.g. proportional to) other parameters, such sensed displacement,acceleration or energy of corresponding sensor components. The inventorshave determined that uni-axial (z axis) sensors are sufficient for thepurposes of detecting rock fall on suitably steep slopes. It will beappreciated that uni-axial sensors are less costly than multi-axialsensors. In some environments or in some applications, however, it maybe desirable to incorporate multi-axial sensors. Accordingly, in someembodiments, sensor 50 may be multi-axial or sensor array 18 maycomprise a plurality of uni-axial sensors oriented in differentdirections. In such embodiments, the number of signals 22 generated by amulti-axial sensor may correspond to its number of axes or the number ofsignals 22 generated by a plurality of uni-axial sensors may correspondto the number of uni-axial sensors.

As discussed above, sensor 50 is electronically connected totransmission line 24 for transmission of a corresponding sensor signal22 to signal processing unit 26. As shown in FIG. 2A, transmission line24 may run through a suitable protective conduit 68, which may be madefrom a suitable material such as suitable plastic, fiber, steel or thelike. In some embodiments, conduit 68 may also house cables 70 (e.g.electrical and/or optical cables) which form part of network connection28 between system 10 and remote workstation 30 and/or other systems 32(see FIG. 1) and/or transmission lines 40 associated with optional imagecapturing devices 34.

FIG. 2B illustrates a sensor 80 which may be incorporated into any oneor more of sensor arrays 18. In FIG. 2B embodiment, sensor 80 is similarin many respects to sensor 50 (FIG. 2A) in that sensor 80 is auni-axial, electromagnetic induction-type sensor. Sensor 80 differs fromsensor 50 in that sensor 80 is mounted (via suitable mounting hardware82) to track 54 as opposed to being a ballast sensor which is spacedapart from track 54. Sensors which are mounted to track section 12(including track(s) 54 and/or ties 56) may be referred to in thisdescription as rail sensors. In other respects, sensor 80 may be similarto sensor 50 described above.

Experiments have determined that rail sensors may be more sensitive todirect contact between falling rocks and track section 12 (e.g. track(s)54 and/or ties 56) and may be more sensitive to passing trains orhighrail vehicles. In some embodiments, therefore, it is desirable toinclude one or more rail sensors. However, in some embodiments, it isdesirable to include ballast sensors rather than rail sensors or onlyballast sensors, because: ballast sensors may be less prone to damage bytrains passing along track section 12, ballast sensors may be morerobust to maintenance of track section 12 which may involve physicalmanipulation of track section 12 (e.g. lifting track section 12 awayfrom ballast 52), ballast sensors may produce more uniform signals,ballast sensors may exhibit greater differences in spatial attenuationand may therefore lead to more accurate location of the hypocenter ofrock fall events and ballast sensors may be less sensitive to highfrequency vibrations which may permit lower sampling rates andcorrespondingly higher bit resolution for the same data acquisitionhardware.

FIG. 3 is a schematic illustration of a signal processing unit 26according to a particular embodiment which is suitable for use with rockfall detection system 10. In the illustrated embodiment, signalprocessing unit 26 comprises a plurality m of inputs 100 correspondingto transmission lines 24 and signals 22 from sensor arrays 18. Eachinput signal 100 is provided to corresponding signal conditioningcircuitry 102. Suitable signal conditioning circuitry 102 is well knownto those skilled in the art and, by way of non-limiting example, maycomprise anti-aliasing filter(s) and amplifier(s). Conditioned sensorsignals 104 are then provided to analog-to-digital converters (ADCs)106. ADCs 106 sample conditioned sensor signals 104 and providecorresponding digital sensor signals 108. In one particular embodiment,ADCs 106 provide 24 bits of digital resolution (i.e. digital sensorsignals 108 comprise a sequence of 24 bit samples), but this is notnecessary. In other embodiments, ADCs 106 may output digital sensorsignals 108 having other suitable bit depths. The sampling rate of ADCs106 may be selected to be sufficiently fast to accommodate thefrequencies of interest, as described in more detail below.

Digital sensor signals 108 output from ADCs 106 are provided to datalogger 110. In addition to receiving digital sensor signals 108, datalogger 110 also receives timing synchronization signal 112 from timingsynchronization source 114. In one particular embodiment, timingsynchronization source 114 comprises a global positioning satellite(GPS) receiver which receives timing information from one or moresatellite sources. A GPS-based timing synchronization source 114 isparticularly useful in embodiments, where system 10 is a modularcomponent system of a larger system that includes other componentsystem(s) 32 (FIG. 1), which other component systems 32 may have theirown signal processing units 26 and their own timing synchronizationsources 114. In such systems, GPS-based timing synchronization sources114 could provide synchronous timing signals 112 across modularcomponent system 10 and other component systems 32. In otherembodiments, where there is only one signal processing unit 26, timingsynchronization source 114 may comprise one or more other sources oftiming information. By way of non-limiting example, timingsynchronization source 114 may access timing information from aninternal or external quartz piezo-electric oscillator, timingsynchronization source 114 may comprise a real time clock or a suitablehardware timing chip or the like.

Using timing synchronization signal 112 and digital sensors signals 108,data logger 110 time stamps, collects and logs the data generated bysensor arrays 118 (FIG. 1). Data logger 110 may have access to memory(not expressly shown) and may use any suitable data structure(s) ordatabase protocol(s) for logging digital sensor signals 108 andcorresponding time stamp information from synchronization signal 112.Data logger 110 may store information in a manner that is indexed, orotherwise accessible, by time stamp indicia, by corresponding sensor,and/or by occurrence of an event (as explained in more detail below). Insome embodiments, data logger 110 may be operatively connected (vianetwork interface 122 and network connection 28) to remote workstation30 and/or to other systems 32 (see FIG. 1). Processor 120 and/or datalogger 110 may perform data compression to save local storage spaceand/or network bandwidth. In the illustrated embodiment, data logger 110is also operatively connected (via interface 118) to embedded dataprocessor 120.

In some embodiments, signal conditioning circuitry 102, ADCs 106, and/ordata logger 110 may be implemented by a data acquisition unit (DAU) 116.Various DAUs are known to those skilled in the art and are commerciallyavailable from a number of sources. In some embodiments, DAU 116 mayalso incorporate its own timing synchronization source 114. In someembodiments, DAU 116 may include other components which are notexpressly shown in the FIG. 3 illustration. By way of non-limitingexample, such components may include digital processing components (e.g.digital filters) or the like. Suitable DAUs include, by way ofnon-limiting example, the TMA-24 Microseismic Acquisition Unit availablefrom Terrascience Systems Ltd. of Vancouver, Canada and other suitableDAUs. In some embodiments, it is desirable that the DAU sample at a rategreater than or equal to 500 Hz with a bid resolution of 16 or morebits.

Commercially available DAUs 116 may have a limited number of inputs 100or a limited data storage capacity. In some embodiments, therefore,signal processing unit 26 may comprise a plurality of DAUs 16, each ofwhich may be configured in a manner similar to that described herein.

Data logger 110 is operatively connected (via interface 118) to dataprocessor 120. Data processor 120 may be part of a suitably configuredcomputer system (not shown) or may be part of an embedded system.Processor 120 shown schematically in FIG. 3 may comprise more than oneindividual data processor which may be centrally located and/ordistributed. Processor 120 may comprise internal memory (not shown)and/or have access to external memory 128. Processor 120 may beprogrammed with, or otherwise have access to, software 124. As explainedin more detail below, processor 120 may execute software 124 which mayin turn cause processor 120 to process data obtained from data logger110 and to generate one or more outputs 126. Processor 120 may alsocontrol the operation of DAU 116, data logger 110 and/or system 10 viainterface 118. In some embodiments, processor 120 may be operativelyconnected (via network interface 122 and network connection 28) toremote workstation 30 and/or to other systems 32 (see FIG. 1). Processor120 may output some or all of outputs 126 to remote workstation 30and/or to other systems 32 via network interface 122 and networkconnection 28.

In embodiments where system 10 includes optional image capturing devices34, signal processing unit 26 may also comprise image data memory 130for storing image data 36 captured by image capturing devices 34. Imagedata 36 may be delivered to image data memory 130 along transmissionlines 40 as shown in the illustrated embodiment or may be wirelesslydelivered to image data memory 130 using a wireless transceiver (notshown). Data processor 120 may also control image capturing devices 34using camera control signals 38 which may be transmitted to imagecapturing devices 34 along transmission lines 40 and/or wirelessly.Camera control signals 38 may permit image capturing devices 34 to move(e.g. pan), zoom, focus or the like and may control when and how imagecapturing devices 34 capture image data 36.

FIG. 4A is a plot showing digitized and time stamped sensor dataobtained at processor 120 for a number of sensors (e.g. sensors 50)within sensor arrays 18. The vertical axis of the FIG. 4A plot ismeasured in binary counts (e.g. digital values output by ADCs 106 andstored in data logger 110) and the horizontal axis of the FIG. 4A plotis measured in milliseconds (ms). As discussed above, acoustic energysensors within sensor arrays 18 may output signals 22 that are generallycorrelated with sensed velocity of a sensor element. In suchembodiments, the binary counts on the vertical axis of the FIG. 4A plotmay also be correlated with this velocity. Where the acoustic energysensors within sensor arrays 18 represent other parameters (e.g.displacement, acceleration, energy), the binary counts on the verticalaxis of the FIG. 4A plot may be correlated with such other parameters.It should be noted that the scales of the vertical axis for theindividual sensor plots within FIG. 4A are different for eachsensor—i.e. the plots corresponding to sensors #1, #(m−1) and #m haveranges of approximately (−100,100) binary counts, the plotscorresponding to sensors #2 and #4 have ranges of approximately(−2500,2500) and the plot corresponding to sensor #3 has a range ofapproximately (−5000,5000).

FIG. 4A shows typical digitized and time-stamped sensor data obtained atprocessor 120 for a small event that is detected in a region of sensors#2, #3 and #4 at a time around 3,000-7,000 ms. It can be seen that, inthe 3,000-7,000 ms time period, the magnitude of the sensed signals ofsensors #2, #3 and #4 (on the order of thousands of binary counts) issignificantly greater than the background noise (on the order ofhundreds of binary counts). This event is typical of a small scale rockfall, but may also be typical of other small scale events, such as (byway of non-limiting example): raveling of a rock face, a surge inadjacent waterfall activity, one or more animals, vegetation or fencingshaken by wind or the like. For typical applications alongside railwaystracks, the scale of the FIG. 4A event may be interpreted to besufficiently small that it is not of significant concern.

FIG. 4B shows typical digitized and time-stamped sensor data obtained atprocessor 120 for a passing train. Like FIG. 4A, the vertical axis ofthe FIG. 4B plot is measured in binary counts and the horizontal axis ismeasured in milliseconds. However in the FIG. 4B plot, the vertical axesfor each sensor are on the same scale (−2×10⁶,2×10⁶). It can be seenthat the duration of the train event is significantly longer than theevent of FIG. 4A.

FIG. 4C shows typical digitized and time-stamped sensor data obtained atprocessor 120 for a passing highrail vehicle. The vertical axis of theFIG. 4C plot is measured in binary counts and the horizontal axis ismeasured in milliseconds. While the vertical scales vary between theindividual FIG. 4C plots for the individual sensors, it can be seen thatthe scales of the individual FIG. 4C plots for the highrail vehicle havescales that are lower than those of FIG. 4B for the train.

In some environments, there may be additional sources of events whichmay be particular of the environment in which system 10 is deployed. Oneexample of a such an event is the activation of a power generator in avicinity of system 10. Where it is desired for system 10 to operate at aremote location, such a generator may be used to power system 10 itself.Such a generator is not required however and other sources of power(e.g. batteries, solar power or wind power) may be used to power system10. FIG. 4D shows typical digitized and time-stamped sensor dataobtained at processor 120 for the activation of a power generator in thevicinity of system 10. The vertical axis of the FIG. 4D plot is measuredin binary counts and the horizontal axis is measured in milliseconds.The vertical axes for each sensor in the FIG. 4D plots is on the samescale (−4000,4000).

FIG. 4E shows typical digitized and time-stamped sensor data obtained atprocessor 120 for a rock fall event that is of sufficient size to be ofconcern for typical railway applications. The vertical axis of the FIG.4E plot is measured in binary counts and the horizontal axis is measuredin milliseconds. It will be noted that the vertical scales vary betweenthe individual FIG. 4E plots for the individual sensors. FIG. 4Eindicates that the rock fall event took place between approximately19,000-21,000 ms. Comparing the vertical scales of the various sensorsand the corresponding magnitudes of the sensed signals, it would appearthat the rock fall event occurred relatively closer to sensor #m than toany of the other illustrated sensors. Comparing the vertical scales andthe corresponding magnitudes of the sensed signals between the FIG. 4Erock fall event and the FIG. 4A small scale event, it can be seen thatthe FIG. 4E rock fall event has significantly greater magnitude.

In some embodiments, rock fall detection by system 10 may be performedby signal processing unit 26 based on signals 22 received from sensorarrays 18 (see FIG. 1). In particular embodiments, rock fall detectionby system 10 may involve processor 120 processing data from data logger110 (or DAU 116) to detect rock fall events (see FIG. 3). In otherembodiments, rock fall detection may be performed at remote workstation30 and/or at other systems 32 having access to data from data logger 110(or DAU 116) via network connection 28. For the remainder of thisdescription, it is assumed, without loss of generality, that rock falldetection is performed by embedded processor 120 processing datareceived from data logger 110.

A part of rock fall detection performed by system 10 involvesdiscriminating between rock fall events and other types of events whichmay be of less concern and/or between significant rock fall events andrelatively small rock fall events which may be of less concern. An eventthat is determined by system 10 to be a significant rock fall event, butwhich in fact is a different event (e.g. a moving train, a train thathas come to a stop in a vicinity of track section 12, a moving highrailvehicle, a highrail vehicle that has come to a stop in a vicinity oftrack section 12 (e.g. to perform maintenance on track section 12), ananimal in a vicinity of track section 12 and/or a power generator) or isan insignificant rock fall event may be referred to in this descriptionas a false positive detection result. In general, there is a desire tominimize false positive detection results.

To detect rock fall events while minimizing false positive detectionresults, processor 120 may process data received from data logger 110 todetermine a plurality of processing parameters. Some or all of theseprocessing parameters may be used in turn to discriminate rock fallevents from other events. FIG. 5 schematically illustrates a number ofprocessing parameters 150 which may be determined by processor 120 usingdata accessed from data logger 110. Each of these processing parameters150 is explained in more detail below. In some embodiments, processor120 may output one or more of processing parameters 150 as outputs 126.As discussed above, some or all of outputs 126 may be available toremote workstation 30 and/or other systems 32 via network connection 28.

Processor 120 may process the data corresponding to one or more sensorsto determine a ratio of a short term average (STA) to a long termaverage (LTA), which may be one of the processing parameters 150. Thisratio may be referred to as an STA/LTA average and may be computedaccording to:

$\begin{matrix}{{\left( \frac{STA}{LTA} \right)_{n} = \frac{\frac{\sum\limits_{i = {({n - {({a - 1})}})}}^{i = n}{x_{i}}}{a}}{\frac{\sum\limits_{i = {({n - {({b - 1})}})}}^{i = n}{x_{i}}}{b}}}{where}{b > a > 0}{and}{{b \geq a},b}} & (1)\end{matrix}$

where: x_(i) represents the value of the sample, n is the index of thecurrent sample x_(n), a is the STA duration (number of samples) and b isthe LTA duration (number of samples). Examining equation (1), it will beappreciated that the STA and LTA durations a and b may be expressed asnumbers of samples or equivalently as temporal durations.

FIGS. 6A and 6B respectively depict typical digitized, time-stampedsensor data obtained at processor 120 for a rock fall event and thecorresponding STA/LTA ratio. For the FIG. 6B plots, the STA duration ais 20 ms and the LTA duration b is 1000 ms. It can be seen from theFIGS. 6A and 6B plots that the rock fall event occurs around the1250-1750 ms time period.

The STA/LTA ratio is useful for detecting when a signal changes to standout from background noise and may therefore be compared against asuitable threshold to trigger the start and end of an event. Forexample, when the LTA/STA ratio is greater than an event start threshold(thresh_start), then processor 120 may determine that an event hasstarted and the associated time t_(start). Similarly, when an event hasstarted and the LTA/STA ratio falls below an event end threshold(thresh_end), then processor 120 may determine that an event has endedand the associated time t_(end). The STA/LTA threshold parametersthresh_start, thresh_end may be experimentally determined as a part ofthe calibration of system 10 and may depend, by way of non-limitingexample, on the STA averaging duration a, the LTA averaging duration b,the spectral characteristics (e.g. amplitude and dominant frequencies)of the background noise in a vicinity of track section 12 and/or theexpected spectral characteristics (e.g. amplitude and dominantfrequencies) of an event that system 10 is designed to detect. Thesethreshold parameters may additionally or alternatively be useradjustable.

The start and end times t_(start), t_(end) of an event can also be usedto determine the event duration t_(dur) as one of the FIG. 5 processingparameters 150 according to:

t _(dur) =t _(end) −t _(start)  (2)

An issue which may arise with the STA/LTA ratio is the so called“memory” associated with the LTA. The LTA value computed by processor120 carries with it information about the last b samples (where b is theLTA duration used in equation (1)). In some cases, the last b sampleswill be influenced by an event. For example, when a train passes tracksection 12, it typically takes a period of time for the train to pass.In such cases, the last b samples used to compute the LTA may beinfluenced by the signal associated with the passing train—e.g. the LTAmay be relatively large during, or even after, a passing train. In suchcircumstances, the relatively high LTA may cause the STA/LTA ratio tolose sensitivity, even if the STA is relatively high.

In some embodiments, therefore, processor 120 processes the data fromdata logger 110 to determine a modified STA/LTA ratio as one ofprocessing parameters 150. This modified STA/LTA ratio may involvereplacing the actual LTA with a constant c according to:

$\begin{matrix}{{\left( \frac{STA}{LTA} \right)_{{mod},n} = {\sum\limits_{i = {({n - {({a - 1})}})}}^{i = n}x_{i}}}{where}{n > a > 0}} & (3)\end{matrix}$

The constant c may be representative of the LTA during event free times(e.g. times without rock fall or passing trains or the like) and, insome embodiments, may be determined during calibration of system 10. Forexample, the constant c may be determined in a relatively noise freeperiod in the environment where system 10 is deployed prior to theactual deployment of system 10. In one particular embodiment, theconstant c may be determined to be an actual LTA during such a noisefree period (e.g. determined according to the denominator of equation(1)). The constant c may be user adjustable.

The modified STA/LTA ratio (equation (3)) may be used in substantiallythe same manner as the actual STA/LTA ratio (equation (1)) to determinethe start and end of an event and the associated times t_(start),t_(end) and to determine the associated event duration t_(dur). In someembodiments, the modified STA/LTA ratio may be used in addition to or asan alternative to the actual STA/LTA ratio. In some embodiments, thethresholding decision associated with the start and/or end of an eventmay involve a compound decision wherein both the modified and actualSTA/LTA ratios are subject to threshold conditions. In some embodiments,the decision as to whether to use the actual STA/LTA ratio, the modifiedSTA/LTA ratio or both (i.e. to determine the start and end of an eventand the associated times t_(start), t_(end) and to determine theassociated event duration t_(dur)) may be a user-selectable parameter.

Another processing parameter 150 that may be determined by processor 120based on data from data logger 110 may be referred to as a peak particlevelocity (PPV). The PPV may represent the magnitude of the sample withthe largest absolute value during an event and may be determinedaccording to:

PPV=MAX{|x _(i) ∥iεt _(start) . . . t _(end)}  (4)

where MAX{•} is an operator that returns the maximum value of theoperand and x_(i) represents the value of the i^(th) sample.

As discussed above, in the illustrated embodiment, sensor arrays 18comprise one or more acoustic energy sensors (e.g. sensors 50) whichoutput signals 22 correlated with velocity of a sensor component. Assuch, the PPV corresponds to the maximum or peak velocity measured bysuch sensors—hence the term peak particle velocity. In general, however,sensor arrays 18 may comprise acoustic energy sensors which outputsignals 22 correlated with other parameters (e.g. energy, displacementand/or acceleration). In such embodiments, PPV should be understood torepresent the magnitude of the sample with the largest absolute valueduring an event in accordance with equation (4) and need not representvelocity in strict sense. In some embodiments, processor 120 may alsodetermine the time t_(PPV) associated with the PPV. In some embodiments,processor 120 may also determine a global PPV value PPV_(global) whichrepresents the magnitude of the sample with the largest absolute valueover all of the recorded samples—i.e. a PPV which is not limited to thetimes between t_(start) and t_(end) during an event.

System 10 may use PPV to help discriminate between significant rockfalls and other types of events. In one particular embodiment, PPV issubjected to a thresholding process which may filter out small rock fallevents, other low magnitude events (e.g. animals) and/or backgroundnoise events (e.g. the operation of a power generator). For example, ifthe PPV of an event is less than a PPV threshold (thresh_PPV), thenprocessor 120 may determine that the event has insufficient magnitude tobe a significant rock fall. The PPV threshold parameter thresh_PPV maybe experimentally determined as a part of the calibration of system 10and may depend, by way of non-limiting example, on particular minimummagnitude rock fall detection required of system 10, the expectedmagnitude of low magnitude events (e.g. animals or humans), the expectedmagnitude of background events (e.g. power stations, waterfalls, wind)and/or the like. The PPV threshold parameter thresh_PPV may additionallyor alternatively be user adjustable.

Another processing parameter 150 that may be determined by processor 120based on data from data logger 110 may be referred to as a the signalenergy E. In some embodiments, the signal energy E used by system 10 mayrepresent a windowed average of the sample amplitude squared and may bedetermined according to:

$\begin{matrix}{E_{n} = \frac{\sum\limits_{i = {n - {({d - 1})}}}^{n}\left( x_{i} \right)^{2}}{d}} & (5)\end{matrix}$

where: x_(i) represents the value of the i^(th) sample, n is the indexof the current sample x_(n) and d is the window duration (number ofsamples). Examining equation (5), it will be appreciated that theduration d may be expressed as numbers of samples or equivalently astemporal durations.

Like the STA/LTA ratio discussed above, the signal energy E is usefulfor detecting when a signal changes to stand out from background noiseand may therefore be compared against suitable thresholds to trigger thestart and end of an event. For example, when the signal energy E isgreater than an event start threshold (E_thresh_start), then processor120 may determine that an event has started and the associated timet_(start). Similarly, when an event has started and the signal energy Efalls below an event end threshold (E_thresh_end), then processor 120may determine that an event has ended and the associated time t_(end).The threshold parameters E_thresh_start, E_thresh_end may beexperimentally determined as a part of the calibration of system 10 andmay depend, by way of non-limiting example, on the duration d of theenergy window, the spectral characteristics (e.g. amplitude and dominantfrequencies) of the background noise in a vicinity of track section 12and/or the expected spectral characteristics (e.g. amplitude anddominant frequencies) of an event that system 10 is designed to detect.These threshold parameters may additionally or alternatively be useradjustable. The start and end times t_(start), t_(end) of an event canalso be used to determine the event duration t_(dur) as described above(equation (2)).

The signal energy E may also be used in addition to or in thealternative to the STA/LTA ratio in other circumstances where it mightbe appropriate to use the STA/LTA ratio. The maximum signal energyE_(max)=MAX{E_(i)|iεt_(start) . . . t_(end)} also exhibits a correlationwith the PPV discussed above. In some embodiments, the maximum signalenergy E_(max) may be used in addition to or in the alternative to thePPV value in circumstances where it might be appropriate to use the PPVvalue.

Another processing parameter 150 that may be determined by processor 120based on data from data logger 110 is the spectral power distribution(e.g. frequency content) of a signal corresponding to an event. In oneparticular embodiment, processor employs a Fast Fourier Transform (FFT)technique to the sampled data during an event (i.e. between t_(start)and t_(end)). The spectral power may therefore be referred to in thisdescription as the FFT. As is known in the art, however, there are anumber of FFT techniques and other techniques for determining thetime-frequency content of a digitally sampled signal and any suchtechniques may be used to determine the time-frequency content of asignal.

FIGS. 6C and 6D respectively show a 0.4 second segment of a timestamped, digital sensor signal received at processor 120 and acorresponding FFT associated with a typical rock fall event. FIG. 6Dshow that most of the spectral power of the digital sensor signalsassociated with a typical rock fall event is concentrated in thefrequency band less than 125 Hz. FIGS. 6E and 6F respectively show a 0.4second segment of a time stamped, digital sensor signal received atprocessor 120 and a corresponding FFT associated with a typical trainevent. FIG. 6F shows that the spectral power of the digital sensorsignals associated with a typical train event is spread over 0-400 Hzand has significant power at frequencies over 200 Hz. FIGS. 6G and 6Hrespectively show a 1.0 second segment of a time stamped, digital sensorsignal received at processor 120 and a corresponding FFT associated witha typical highrail vehicle event. FIG. 6H shows that the spectral powerof the digital sensor signals associated with a typical highrail vehicleevent (like a train event) is spread over 0-400 Hz and has significantpower at frequencies over 200 Hz. The sensor data shown in FIGS. 6C-6His obtained from representative rail sensors, but the inventors haveconcluded that similar results are achievable with suitably configuredballast sensors.

FIGS. 6I and 6J respectively show an 11 second segment of a timestamped, digital sensor signal received at processor 120 and acorresponding FFT associated with the operation of a generator in avicinity of track section 12. FIG. 6J shows that the spectral power ofthe digital sensor signals associated with the generator has a uniquefrequency signature with harmonics at 30.66 Hz, 45.95 Hz and 60 Hz.

FIG. 7A schematically depicts a method 200 for event detection accordingto a particular embodiment. Method 200 may be performed in whole or inpart by embedded processor 120. Method 200 may make use of data obtainedfrom data logger 110 and/or DAU 116 and may also make use of processingparameters 150. As discussed above, in other embodiments, rock falldetection (including method 200 in whole or in part) may be performed byother processors, such as by processors associated with remoteworkstation 30 and/or other systems 32.

Method 200 starts at block 201. Method 200 may involve a number ofprocedures which are similar for the data associated with eachsensor—e.g. to each particular digital sensor signal 108 (FIG. 3). Inthe illustrated embodiment, these similar procedures are shown by therepresentative procedures of block 202 (associated with sensor #1) andblock 204 (associated with sensor #m). It will be appreciated that,depending on the number of sensors and the corresponding number ofdigital sensor signals 108, method 200 may generally comprise anysuitable number of procedures similar to those of blocks 202, 204. Theprocedure of block 202 is now described in more detail, it beingunderstood that the procedure associated with block 204 and othersimilar blocks may be substantially similar to that of block 202.

The block 202 procedure starts in block 210 which involves initializinga number of parameters. For example, block 210 may involve obtainingsufficient number of data samples (a) to calculate the STA (thenumerator of equation (1) and/or equation (3)) and/or a sufficientnumber of data samples (b) to calculate the LTA (the denominator ofequation (1)). Such data samples may be taken from the digital signalsensor signal 108 associated with block 202. Block 210 may involveresetting a number of the processing parameters 150 which may have beenused during previous post event processing (described in more detailbelow). Block 210 may also involve initializing one or more calibrationparameters and/or user-configurable parameters. The procedure of block202 then proceeds to block 215, which involves obtaining the next datasample—e.g. the next data sample from the digital sensor signal 108associated with block 202.

In block 220, block 202 may involve updating one or more processingparameters 150 based on the newly acquired block 215 data and, in someinstances, the historical data obtained prior to the current iterationof block 215. In particular embodiments, the particular processingparameters 150 which are updated in block 215 include those associatedwith event-start triggering criteria. As explained above, processingparameters 150 associated with triggering the start of an event mayinclude: the STA/LTA ratio (equation (1)), the modified STA/LTA ratio(equation (3)) and/or the energy (equation (5)).

Block 225 involves evaluating event-start criteria. The block 225event-start criteria may involve an evaluation of whether one or moreprocessing parameters (e.g. the STA/LTA ratio (equation (1)), themodified STA/LTA ratio (equation (3)) and/or the energy (equation (5))are greater than one or more corresponding event-start thresholds (e.g.thresh_start_((STA/LTA)), thresh_start_((STA/LTA)mod),thresh_start_((E))). If the block 225 evaluation of the event-startcriteria is negative (block 225 NO output), then the procedure of block202 loops back to block 215 to obtain another data sample. If on theother hand the block 225 evaluation of the event-start criteria ispositive (block 225 YES output), then the procedure of block 202proceeds to block 230.

Block 230 involves setting a value for t_(start). In particularembodiments, the block 230 t_(start) value may be based on the timeassociated with the current block 215 data sample. The procedure ofblock 202 then proceeds to blocks 235 and 240 which involve obtainingthe next data sample and updating one or more processing parameters in amanner similar to that of blocks 215 and 220 described above.

Block 245 then involves evaluating event-end criteria. The block 245event-end criteria may involve an evaluation of whether one or moreprocessing parameters (e.g. the STA/LTA ratio (equation (1)), themodified STA/LTA ratio (equation (3)) and/or the energy (equation (5))are less than one or more corresponding event-end thresholds (e.g.thresh_end_((STA/LTA)), thresh_end_((STA/LTA)mod), thresh_end_((E))). Ifthe block 245 evaluation of the event-end criteria is negative, then theblock 202 procedure loops back to block 235 to obtain another datasample. If on the other hand the block 245 evaluation of the event-endcriteria is positive, then block 202 procedure determines that the eventhas ended and proceeds to block 250, which involves setting a value forthe event end time t_(end). In particular embodiments, the block 250t_(end) value may be based on the time associated with the current block235 data sample.

In the illustrated embodiment, the block 202 procedure then proceeds toblock 255, which involves post event processing. The post eventprocessing of block 255 may involve discriminating between types ofevents or otherwise determining whether a particular event is asignificant rock fall event. In the illustrated embodiment of method200, the block 255 post event processing is shown within the block 202procedure—i.e. the block 255 post event processing may be performed foreach sensor whose digital signal 108 triggers the detection of an event.This is not necessary. In some embodiments, the block 255 post eventprocessing may be performed outside of the block 202 procedure—e.g. theblock 255 post event processing may be performed on a global basisand/or for a subset of the sensors whose digital signals 108 trigger thedetection of an event.

FIG. 7B schematically depicts a method 300 for post event processingwhich may be performed in block 255 according to a particularembodiment. In the illustrated embodiment, the post event processing ofmethod 300 involves discriminating between a number (e.g. six) ofdifferent types of events. In other embodiments, the post eventprocessing may involve discriminating between two types of events—i.e.significant rock fall events and any other kind of event. In someembodiments, method 300 may be performed for each sensor whose digitalsensor signal 108 triggers the detection of an event. In otherembodiments, method 300 may be performed for a subset of the sensorswhose digital sensor signals 108 trigger the detection of an event.

In the illustrated embodiment, method 300 starts in block 305 whichinvolves determining one or more event specific processing parameters150. Once t_(start) and t_(end) are determined (eg. in blocks 230, 250)for a particular sensor in system 10, processor 120 may obtain a subsetof the associated digital sensor signal 108 which occurs betweent_(start) and t_(end). This data subset may in turn be processed toobtain the block 305 event specific processing parameters 150. Examplesof event specific processing parameters that may be determined in block305 include: the duration t_(dur) of the event which may be determinedaccording to equation (2); the PPV which may be determined according toequation (4); the time (t_(PPV)) associated with the PPV; the spectralpower (FFT) of the discrete signal between t_(start) and t_(end). To theextent that the STA/LTA ratio, the modified STA/LTA ratio or the energyare not determined in the block 202 procedure, then any one or more ofthese quantities (and/or their associated maxima, STA/LTA_(max),STA/LTA_(mod) _(—) _(max), E_(max) and the times of their associatedmaxima) may also be determined in block 305.

Method 300 then proceeds to block 310 which involves evaluating eventduration criteria. The block 310 evaluation may involve comparing theevent duration t_(dur) to a threshold (thresh_dur) to determine whetherthe event duration t_(dur) is less than the threshold (thresh_dur). Insome embodiments, the event duration threshold (thresh_dur) may be in arange of 1-3 seconds. In other embodiments, this range may be 2-10seconds. The magnitude of the event duration threshold (thresh_dur) maydepend on the typical length of the trains that pass through tracksection 12.

If t_(dur) is greater than the event duration threshold (thresh_dur),then method 300 may proceed along the block 310 NO output to block 315.In the illustrated embodiment, block 315 involves concluding that theevent is either a passing train or a passing highrail vehicle. Fromblock 315, method 300 proceeds to block 320 which involves an evaluationof a magnitude criteria to determine whether the event was triggered bya passing train (block 320 YES output and the conclusion of block 325)or the event was triggered by a highrail vehicle (block 320 NO outputand the conclusion of block 330). The block 320 magnitude evaluation mayinvolve comparing the PPV of the associated digital sensor signal 108 toa suitable threshold. If the PPV is greater than the block 320threshold, then the event is determined to be a train (block 320 YESoutput and block 325 conclusion), whereas if the PPV is less than theblock 320 threshold, then the event is determined to be a highrailvehicle (block 320 NO output and block 330 conclusion). Depending on thegeological site conditions, in some embodiments block 320 mayadditionally or alternatively involve an evaluation of spectral criteria(e.g. comparing the FFT of an event to one or more thresholds). Suchspectral criteria may be used as an alternative to or in addition to theblock 320 magnitude criteria to discriminate a train event from ahighrail event.

Returning to the block 310 evaluation, if t_(dur) is less than the eventduration threshold (thresh_dur), then method 300 may proceed along theblock 310 YES output to optional block 335. Block 335 involves theoptional evaluation of spectral criteria to determine whether an eventwas triggered by a passing train or highrail vehicle. As discussedabove, depending on the geological conditions in a vicinity of tracksection 12, suitably configured sensors (e.g. ballast sensor 50 of FIG.2A and/or rail sensor 80 of FIG. 2B) may generate distinctive frequencycharacteristics in response to trains and/or highrail vehicles travelingon track section 12. These distinctive frequency characteristics may beused to discriminate trains or highrail vehicles from other types ofevents. In one particular embodiment, the block 335 spectral criteriainvolves determining whether the FFT associated with a digital sensorsignal 108 has a significant amount (e.g. x % or more) of its power atfrequencies greater than a frequency threshold (thresh_freq). In oneparticular embodiment, this threshold may be in a range of 100 Hz-300Hz. In another embodiment, this threshold may be in a range of 125Hz-200 Hz. In one particular embodiment, the significant amount (e.g. x% or more) may be in a range of 0%-25%. In other embodiments, thesignificant amount (e.g. x % or more) may be in a range of 5%-15%.

If the FFT of the rail sensor has a significant amount (e.g. x % ormore) of its power at frequencies greater than a frequency threshold(thresh_freq), then the block 335 evaluation is positive (YES output)and method 300 proceeds to block 340 which involves concluding that theevent is either a passing train or a passing highrail vehicle. Blocks340, 345, 350 and 355 may be substantially similar to blocks 315, 320,325 and 330 discussed above and may involve discriminating between atrain (block 350 conclusion) and a highrail vehicle (block 355conclusion).

If, on the other hand, the FFT of the rail sensor does not have asignificant amount (e.g. x % or more) of its power at frequenciesgreater than a frequency threshold (thresh_freq), then the block 335evaluation is negative (NO output) and method 300 proceeds to block 360which involves evaluation of magnitude criteria to determine whether theevent in question is a significant rock fall event—i.e. a rock fallevent worthy of concern. The block 360 magnitude evaluation may involvecomparing the PPV of the associated digital sensor signal to a suitablethreshold (thresh_PPV). In some embodiments, this magnitude threshold(thresh_PPV) may be in a range of 500-5000 bits. In other embodiments,this range may be 750-2,500 bits. If the PPV is less than the block 360threshold (thresh_PPV), then the event is determined to be aninsignificant event (block 360 NO output and block 365 conclusion).

In the illustrated embodiment, however, method 300 goes beyond the block365 conclusion of classifying the event as an insignificant event. Asdiscussed above, it may be desirable to discriminate other types ofnatural or human-made noise that may trigger events in a vicinity oftrack section 12. In one particular embodiment, a generator (not shown)is located in a vicinity of track section 12. When the generator turnson, it may trigger an event on one or more sensors of system 10. In theillustrated embodiment, method 300 proceeds from block 365 to block 370which involves evaluation of spectral criteria. As explained above, thespectral power associated with the start up and operation of thegenerator has a particular spectral pattern. Accordingly, spectralcriteria can be designed for the block 370 inquiry to determine whetherthe event was triggered by the generator. Such spectral criteria mayinvolve evaluation of whether the FFT of the associated digital sensorsignal 108 has a significant amount (e.g. y % or more) of its power atfrequencies within particular frequency bands associated with the startup and operation of the generator. If the block 370 evaluation ispositive (block 370 YES output), then method 300 concludes that theevent was triggered by the generator in block 375.

It should be noted that the block 370 spectral evaluation and the block375 conclusion that the event was triggered by a generator represent onenon-limiting example of the type of criteria which may be used todiscriminate other types of natural or human-made surface noise that maytrigger events in a vicinity of track section 12. In other embodiments,it might be desirable to use additional or alternative criteria (e.g. inblock 370 or in other similar inquiries) to discriminate additional oralternative surface noise events. Such surface noise events may include(by way of non-limiting example): noise created by moving water (e.g.waterfalls, rivers or the like); noise created by animals; noise createdby nearby traffic; noise created by falling trees; noise created bytrains or highrail vehicles that have come to a stop in a vicinity oftrack section 12; and/or the like. The types of criteria used todiscriminate these events may include (by way of non-limiting example):magnitude criteria, spectral criteria, duration criteria, correlationcriteria and/or the like. It is not necessary that the evaluation ofthese additional or alternative criteria occur in any particular orderrelative to the other method 300 criteria evaluations. In general, themethod 300 criteria evaluations can occur in any desirable order. Forexample, if it is known that the generator is likely to start every 10minutes and run for 2 minutes, then it may be desirable to locate theblock 370 spectral criteria evaluation at an earlier point within method300 to quickly conclude generator events and to thereby conserveprocessing resources.

If the block 370 evaluation is negative (block 370 NO output), thenmethod 300 proceeds to block 380 which involves an evaluation ofaccumulation criteria to determine whether there has been a sufficientamount of low magnitude rock fall within a sufficiently short period totime to conclude that there has been rock fall accumulation that may beof concern. In one particular embodiment, the block 380 accumulationcriteria involves consideration of whether method 300 has reached block380 (i.e. small rock fall event) more than a threshold number of times(thresh_#) within a recent time period ΔT. By way of non-limitingexample, block 380 may involve evaluating whether method 300 has reachedblock 380 more than 5 times within the last hour. In some embodiments,the block 380 threshold number of times (thresh_#) is in a range of3-50. In other embodiments, this range is 10-20. In some embodiments,the block 380 time period ΔT is in a range of 30-900 minutes. In otherembodiments, this range is 60-480 minutes.

If the block 380 accumulation criteria evaluation is positive (block 380YES output), then method 300 proceeds to block 385 which concludes thatthere has been sufficient rock fall accumulation to be of concern. Ifthe block 390 accumulation criteria evaluation is negative (block 380 NOoutput), then method 300 proceeds to block 390 which concludes that theevent was an insignificant rock fall event.

Returning to the block 360 magnitude evaluation, if the PPV is greaterthan the block 360 threshold (thresh_PPV), then method 300 proceeds (viablock 360 YES output) to block 392 which involves an inquiry intowhether the current event is followed by a train event. The inventorshave determined that when a train passes over track section 12, thepassing train can trigger a number of events (e.g. the train can satisfythe block 225 event start criteria) prior to triggering the principaltrain event. These events which are triggered prior to the principaltrain event may be referred to as train precursor events.

FIG. 8 is a schematic depiction of the triggered state of a number ofsensors in response to a passing train. It can be seen from FIG. 8, thata number of train precursor events 303 occur for each sensor in the timeleading up to the persistent principal train events 304. The inventorshave determined that the time during which train precursor events arelikely to occur is within a time window Δt_(pre-train) prior to theonset of principal train events. In some embodiments, this time windowΔt_(pre-train) is in a range of 5-30 seconds. In other embodiments, thistime window Δt_(pre-train) is in a range of 10-20 seconds.

In some embodiments, the block 392 inquiry as to whether the event isfollowed by a train event may involve an inquiry into whether thecurrent event being processed in method 300 is followed within a timewindow Δt_(pre-train) by a persistent train event. As discussed herein,a persistent train event can be discriminated on the basis of durationcriteria (e.g. block 310), spectral criteria (e.g. block 335), magnitudecriteria (e.g. block 320, block 330), cross-correlation criteria, or anysuitable combination thereof. If the block 392 inquiry is positive (e.g.the current event is followed by a train event within the time windowΔt_(pre-train)—block 392 YES output), then method 300 proceeds to block394 which involves concluding that the current event is a trainprecursor event. If, on the other hand, the block 392 inquiry isnegative (e.g. the current event is not followed by a train event withinthe time window Δt_(pre-train)—block 392 NO output), then method 300proceeds to block 395, where the current event is determined to be asignificant rock fall event.

As discussed above, method 300 (FIG. 7B) represents one possibleembodiment of block 255 of method 200 (FIG. 7A). Returning to FIG. 7A,at the conclusion of block 255 (e.g. method 300), method 200 proceeds toblock 260 which involves an inquiry into whether the block 255 postevent processing associated with any of the sensors reached a conclusionthat the event was a rock fall event (e.g. either the block 390conclusion of method 300 that the event is an insignificant rock fallevent and/or the block 395 conclusion of method 300 that the event is asignificant rock fall event). If the event was not a rock fall event(block 260 NO output), then method 200 proceeds to block 265 whichinvolves taking appropriate action for a non-rock fall event.

The nature of the block 265 action may depend on whether any of thenon-rock fall events are considered to be important for some reason. Theblock 265 action may comprise logging the non-rock fall event or doingnothing. In one particular embodiment, the block 265 action may involvegenerating an event record associated with the non-rock fall event. Therecord of the non-rock fall event may include recordal of a number ofparameters associated with the event. In particular embodiments, theblock 265 record may include one or more of: the event type (e.g. ablock 325, 350 train event, a block 330, 355 highrail vehicle event or ablock 375 surface noise event); a number of triggered sensors; start andend times of the event which may include the start time (t_(start)) forthe first triggered sensor and the end time (t_(end)) for the lastsensor to remain triggered; the PPV and the associated time t_(PPV) foreach triggered sensor; the maxima (and associated times) of one or moreother block 305 event specific parameters (e.g. STA/LTA_(max), E_(max)or the like); and any other parameter of which data processor 120 may beaware. In some embodiments, the block 265 record may also include one ormore of the images of track section 12 which may be captured by cameras34.

In some embodiments, block 265 may involve storing the event record inlocal memory (e.g. in data logger 110, memory 128 and/or image datamemory 130) until such time as signal processing unit 26 is polled forevents (e.g. by remote workstation 30 over network connection 28).Depending on the availability of local memory, in other embodiments,block 265 may involve transmitting the event record (e.g. to remoteworkstation 30 over network connection 28) for remote storage.

If, on the other hand, the event was a rock fall event (block 260 YESoutput), then method 200 proceeds to block 270 which involves estimatinga location of the rock fall event. Block 270 may involve estimating alocation of the rock fall event with a degree of accuracy which is finerthan the minimum spacing 20 between sensor arrays 18 of system 10 (seeFIG. 1). FIG. 7C schematically depicts a method 400 for estimating alocation of a rock fall event which may be performed in block 270according to a particular embodiment. Method 400 commences in block 410which involves selecting a group of sensors to be considered forestimating the rock fall location. In some embodiments, block 410 mayinvolve determining the group of sensors to include all triggeredsensors whose start times t_(start) are within a time window ΔT_(start)of the start time t_(start) of a first sensor to trigger on a rock fallevent.

This block 410 determination is shown schematically in FIGS. 9A and 9B.FIG. 9A shows a typical response of a number of sensors to a rock fallevent. Like FIG. 4E, the vertical axis of FIG. 9A plot is measured inbinary counts and the horizontal axis is measured in milliseconds. Itwill be noted that the plots for the individual sensor signals shown inFIG. 9A are on different vertical scales, but that the particular scalesfor each sensors are omitted for clarity. FIG. 9A also contrasts withFIG. 4E in that FIG. 9A is shown on a much smaller time scale—i.e. FIG.4E spans a time period of 30 s, whereas FIG. 9A spans a time period of2.2 s.

For each sensor of the FIG. 9A plot, FIG. 9B shows when the sensor istriggered—i.e. when the sensor's trigger status is ON. It can be seenfrom FIG. 9A, that the sensor #1 is the first to trigger (at t=t_(start)_(—) _(sensor#1)). Because acoustic waves take time to travel throughthe substrate in the region of track section 12, it may be assumed thatsensor #1 is closest to the hypocenter of the rock fall event. The block410 process for selecting the group of sensors to be considered forestimating the rock fall location may involve selecting all of thesensors which are triggered within a time window ΔT_(start) of t_(start)_(—) _(sensor#1).

This time window ΔT_(start) is shown in FIG. 9B. It can be seen fromFIG. 9B that sensors #2-8 and #12-13 are also triggered within this timewindow ΔT_(start). Accordingly, block 410 may involve selecting sensors#1-8 and #12-13 to be the sensors used for estimating the rock falllocation. FIG. 9B also shows that sensors #9-11 and #14-15 are nottriggered. As such, sensors #9-11 and #14-15 are not selected forestimating the rock fall location in accordance with the illustratedembodiment. Although not explicitly shown in FIG. 9B, sensors which aretriggered after the time window Δt_(start) may be assumed to beindicative of a different event.

The block 410 time window ΔT_(start) may be related to a prediction ofthe average speed of acoustic waves in the earth near track section 12and the length of track section 12 being considered. For example, if thelength of track section 12 being monitored is 1 km and the average speedof acoustic waves in the substrate near track 12 is determined to be 300m/s, then the block 410 time window ΔT_(start) may be set to be 3.3seconds.

Once the block 410 group of sensors is selected method 400 (FIG. 7C)proceeds to block 415 which involves selecting a group of p potentiallocations for the hypocenter of the rock fall event. The p potentialhypocenter locations may be spaced apart from one another by a suitableinterval d which depends on the location detection accuracy desired fromsystem 10. The number p of potential hypocenter locations may depend,for example, on the processing resources associated with system 10 (e.g.associated with embedded processor 120). As discussed above, it islogical to assume that the hypocenter of the rock fall may be mostproximate to the sensor that triggers first (e.g. sensor #1 in theexemplary circumstance of FIGS. 9A and 9B). In particular embodiments,the group of p potential hypocenter locations may be provided in a gridaround the location of the sensor that is triggered first and the gridmay have a spacing of d between potential locations. In someembodiments, the spacing d may be in a range of 1-20 m. In someembodiments, this spacing d is in a range of 2-5 m. In some embodiments,the number p of potential hypocenter locations is in a range of 5-100.In some embodiments, this number p of potential hypocenter locations isin a range of 10-25.

Method 400 then proceeds to loop 420. Loop 420 involves carrying out anumber of procedures for each of the p potential hypocenter locationsdetermined in block 415. In the illustrated embodiment, loop 420 isindexed by the variable i, which may be referred to as the loop counter.The loop counter i starts at i=1 on the first iteration of loop 420 andis incremented by one for each iteration of loop 420 until i=p at whichpoint, method 400 exits from loop 420. Loop 420 commences in block 425which involves selecting the i^(th) potential hypocenter location anddetermining the distances x_(i) between the i^(th) potential hypocenterlocation and the locations of the block 410 sensors. It will beappreciated that where the block 410 group of sensors includes Nsensors, then the quantity x_(i) will be a 1×N vector quantity havingthe form [x₁ _(—) _(i), x₂ _(—) _(i) . . . x_(N) _(—) _(i)]^(T) wherethe notation x_(j) _(—) _(i) indicates the distance between the j^(th)sensor and the i^(th) potential hypocenter location. Once the locationof the i^(th) potential hypocenter location is known, the distancesx_(i) may then be determined based on pre-calibrated or otherwise knownlocations of the sensors of system 10.

Method 300 then proceeds to block 430 which involves determining modelparameters. In one particular embodiment, the spatial attenuation of thePPV of a rock fall event is modeled according to an exponential decaymodel:

y(x)=Ae ^(−Bx)  (6)

where: y(x) represents the PPV amplitude at a distance x from thehypocenter of the rock fall event, A is a model parameter representativeof the PPV at the hypocenter and B is an absorption coefficient modelparameter which may be representative of a quality factor of thesubstrate in the region of track section 12. In accordance with theabove described notation, model equation (6) may be rewritten as:

y_(j)_=A _(i)e^(−B) ^(i) ^(x) ^(j) _i  (7)

where: y_(j) _(—) _(i) is the expected PPV of the j^(th) sensor based ona rock fall at the i^(th) potential hypocenter, x_(j) _(—) _(i) is thedistance between the j^(th) sensor and the i^(th) potential hypocenterlocation, A_(i) is a model parameter representative of the PPV at thei^(th) potential hypocenter and B_(i) is an absorption coefficient modelparameter for the i^(th) potential hypocenter which may berepresentative of a quality factor of the substrate in the region oftrack section 12. In other embodiments, other models may be used for thespatial attenuation of the PPV.

Block 430 then involves solving for the model parameters by minimizing acost function. In embodiments which make use of the model of equations(6) and (7), the model parameters to be determined in block 430 are thequantities A_(i) and B_(i). It will be appreciated that in embodimentswhich use other attenuation models, the model parameters to bedetermined may be different. In one particular example embodiment, thecost function used in block 430 is a least squares cost function which,for the i^(th) potential hypocenter location, may be given by:

$\begin{matrix}{F_{i} = {{\sum\limits_{j = 1}^{N}{w_{j\_ i}\left( r_{j\_ i} \right)}^{2}} = {\sum\limits_{j = 1}^{N}{w_{j\_ i}\left( {y_{j} - {A_{i}^{{- B_{i}}x_{j\_ i}}}} \right)}^{2}}}} & (8)\end{matrix}$

where: F_(i) is the cost function for the i^(th) potential hypocenterlocation, N is the number of block 410 sensors, w_(i) _(—) _(j) is anoptional weighting coefficient for the j^(th) sensor and the i^(th)potential hypocenter location, y_(j) is the actual sensor PPV for thej^(th) sensor and the quantity

r _(j)_i=y _(j) −A _(i) e ^(−B) ^(i) ^(x) _(j-i)

is referred to as the residual for the j^(th) sensor and the i^(th)potential hypocenter location.

The cost function of equation (8) can be minimized when:

$\begin{matrix}{{\frac{\partial F_{i}}{\partial A} = 0}{and}} & \left( {9a} \right) \\{\frac{\partial F_{i}}{\partial B} = 0} & \left( {9b} \right)\end{matrix}$

Equations (9a) and (9b) can be solved for the i^(th) potentialhypocenter location to yield the parameters A_(i) and B_(i).

Solving equations (9a) and (9b) is a non-linear problem which may besimplified (e.g. linearized) by taking the natural logarithm of bothsides of equation (7) to yield:

ln(y _(j) _(—) _(i))=ln(A _(i))−B _(i) x _(j) _(—) _(i) =y′ _(j) _(—)_(i) =A′ _(i) −B _(i) x _(j) _(—) _(i)  (10)

where y′_(j) _(—) _(i)=ln(y_(j) _(—) _(i)) and A′_(i)=ln(A_(i)).Equation (10) represents a linear regression model (as opposed to theexponential regression model of equation (7) and may be used to create aleast squares cost function, which in turn may be minimized to yield thequantities A′_(i) and B_(i). The parameter A_(i) may then be obtainedaccording to A_(i)=e^(A′) ^(i) . Minimizing a least squares costfunction for a linear regression model has a closed form solution and iswell understood by those skilled in the art.

Examining the equation (6) model more closely, it can be seen that thequantity A depends on the amplitude of an event wavelet at thehypocenter and therefore varies from event to event. In contrast, thequantity B represents a quality factor which may depend on thegeotechnical characteristics of the environment around track section 12.It would be expected, therefore, that the quantity B is relativelyconstant from event to event. The inventors have experimentallydetermined (using the least squares curve fitting techniques describedabove) that the parameter B for a particular track section 12 typicallystays within 5-10% of some average value B_(o). Accordingly, in someembodiments, the quantity B_(o) may be determined during calibration andthereafter the parameter B_(i) may be taken as a constant B_(i)=B_(o).In such embodiments, equations (8) and (9a) may be used to solve forA_(i), which is given by:

$\begin{matrix}{A_{i} = \frac{\sum\limits_{j = 1}^{N}{w_{j\_ i}y_{j}^{{- B_{0}}x_{j\_ i}}}}{\sum\limits_{j = 1}^{N}{w_{j\_ i}^{{- 2}B_{0}x_{j\_ i}}}}} & (11)\end{matrix}$

For the equation (6) attenuation model, at the conclusion of block 430,method 400 has determined the model parameters A_(i) and B_(i) for thei^(th) potential hypocenter location. For other models, block 430 mayyield different model parameters for the i^(th) potential hypocenterlocation. Method 400 then proceeds to block 435 which involvesdetermining an error metric associated with the i^(th) potentialhypocenter location. In general, the block 435 error metric may be anysuitable quantity that is representative of the error associated withthe model parameters determined in block 430 for the i^(th) potentialhypocenter. The block 435 error metric may involve a summation ofconstituent error metrics over the N block 410 sensors. Each constituenterror metric may involve a difference between the PPV predicted by theloop 420 model and the PPV measured by the sensor. In embodiments whichmake use of a least squares cost function (e.g. equation (8)), the block435 error metric (E_(i)) may comprise a sum of the squares of theresiduals for the i^(th) potential hypocenter location over the N block410 sensors which may be given by:

$\begin{matrix}{E_{i} = {\sum\limits_{j = i}^{N}\left( r_{j\_ i} \right)^{2}}} & (12)\end{matrix}$

in embodiments which make use of the regression model of equation (7),equation (12) becomes:

$\begin{matrix}{E_{i} = {\sum\limits_{j = 1}^{N}\left( {y_{j} - y_{j\_ i}} \right)^{2}}} & (13)\end{matrix}$

where y_(j) is the PPV value measured at the j^(th) sensor and y_(j)_(—) _(i) is the PPV value predicted by model equation (7) for thej^(th) sensor and the i^(th) potential hypocenter location.

Once the block 435 error metric (E_(i)) is determined, method 400proceeds to block 440 which involves evaluation of a loop exitcondition. If there are other potential hypocenter locations in theblock 415 group of p potential hypocenter locations which have yet to beexamined, then the block 440 inquiry is negative (NO output) and methodloops back to block 450, where the loop counter i is incremented torefer to the next potential hypocenter location and then back to block425. If the procedures of blocks 425, 430 and 435 have been performedfor all of the block 415 group of p potential hypocenter locations, thenthe block 440 loop exit condition is fulfilled (YES output) and method400 proceeds to block 445.

Block 445 involves selecting one of the block 415 group of potentialhypocenters to be the estimated hypocenter of the rock fall event. Inthe illustrated embodiment, the block 445 selection is based on thehypocenter having the lowest error metric (as determined in block 435).In embodiments which use the error metric of equation (12) or (13),block 445 involves selecting the estimated location of the hypocenter ofthe rock fall event to be the potential hypocenter having the lowestvalue of E_(i).

In other embodiments, method 400 may be implemented with a differentattenuation model. For example, in one alternative embodiment, theattenuation model of equation (6) may be replaced by the followingmodel:

$\begin{matrix}{{y(x)} = \frac{A\; ^{- {Bx}}}{\sqrt{x}}} & (14)\end{matrix}$

which represents a combination of absorption and geometric spreading.For the equation (14) model, the equivalent to equation (7) is:

$\begin{matrix}{y_{j\_ i} = \frac{A_{i}^{{- B_{i}}x_{j\_ i}}}{\sqrt{x_{j\_ i}}}} & (15)\end{matrix}$

the least squares cost function equivalent to equation (8) is:

$\begin{matrix}{F_{i} = {{\sum\limits_{j = 1}^{N}{w_{j\_ i}\left( r_{j\_ i} \right)}^{2}} = {\sum\limits_{j = 1}^{N}{w_{j\_ i}\left( {y_{j} - \frac{A_{i}^{{- B_{i}}x_{j\_ i}}}{\sqrt{x_{j\_ i}}}} \right)}^{2}}}} & (16)\end{matrix}$

and the residual r_(j) _(—) _(i) for the j^(th) sensor and the i^(th)potential hypocenter location is given by:

$\begin{matrix}{r_{j\_ i} = {y_{j} - \frac{A_{i}^{{- B_{i}}x_{j\_ i}}}{\sqrt{x_{j\_ i}}}}} & (17)\end{matrix}$

Equation (15) may be linearized by taking the natural logarithm of bothsides to obtain:

ln(y _(j) _(—) _(i)=ln(A _(i))−B _(i) x _(j) _(—) _(i)−½ ln(x _(j) _(—)_(i))=y′ _(j) _(—) _(i) =A′ _(i) −B _(i) x _(j) _(—) _(i)−½ ln(x _(j)_(—) _(i))  (18)

where y′_(j) _(—) _(i)=ln(y_(j) _(—) _(i)) and A′_(i)=ln(A₁). When theassumption is made that B_(i)≈B₀, equations (9a) and (16) may be used tosolve for A_(i) according to:

$\begin{matrix}{A_{i} = \frac{\sum\limits_{j = 1}^{N}{w_{j\_ i}y_{j}\frac{^{{- B_{0}}x_{j\_ i}}}{\sqrt{x_{j\_ i}}}}}{\sum\limits_{j = 1}^{N}{w_{j\_ i}\frac{^{{- 2}B_{0}x_{j\_ i}}}{x_{j\_ i}}}}} & (19)\end{matrix}$

The variables used in the model of equations (14)-(19) may havesubstantially the same meaning as those used in the above describedmodel based on equations (6)-(11). Method 400 using the model set out inequations (14)-(19) may be similar to method 400 described above usingthe model of equations (6)-(11). More particularly, block 430 mayinvolve determining the model parameters A_(i) and B_(i) for the i^(th)potential hypocenter, block 435 may involve determining an error metricassociated with the i^(th) potential hypocenter (e.g. using equations(12) and (13), except that the equation (15) regression model is used inthe place of the equation (7) regression model) and the remainder ofmethod 400 may be substantially similar to that described above.

As discussed above, method 400 (FIG. 7C) represents one possibleembodiment of block 270 of method 200 (FIG. 7A). The block 445 estimatedhypocenter location may be the output of the block 270 event locationestimation. In other embodiments, block 270 may be implemented by othermethods. Returning to FIG. 7A, at the conclusion of block 270 (e.g.method 400), method 200 may proceed to optional block 275 which involvesestimating an event energy. The optional block 275 energy estimation mayalso be based on the spatial attenuation model used in block 270 toestimate the rock fall hypocenter. In the particular exemplaryembodiment described in method 400 above, the spatial attenuation modelis represented by equations (6) and (7).

If it is assumed that the trajectory associated with a falling rock ispredominantly vertical, then the rock's kinetic energy may be expressedas:

KE=mhg  (14)

where: m is the mass of the rock, h is the height from which the rockfalls and g is the acceleration due to gravity. The block 275 energyestimation may also involve the assumption that the PPV of a rock fallevent at the hypocenter is proportional to the kinetic energy KE:

KE=kA  (15)

where: A is the value of the model parameter A_(i) determined in block430 and associated with the hypocenter selected in block 445 and k is aconstant of proportionality which may be determined experimentallyduring calibration of system 10. The block determination of the energyassociated with a rock fall event may be determined using equation (15).

Method 200 (FIG. 7A) proceeds to block 280 which involves taking theappropriate action for a rock fall event. FIG. 7D schematicallyillustrates a method 500 for taking appropriate action in respect of arock fall event which may be performed in block 280 according to aparticular embodiment. Method 500 commences in optional block 510 whichinvolves obtaining one or more images of the estimated event location.The estimated event location may be the event location determined inblock 270 (e.g. the block 445 hypocenter). Optional block 510 mayinvolve controlling one or more image capturing devices 34 using cameracontrol signals 38. Image capturing device(s) 34 may be controlled, soas to direct them toward the estimated event location and to capturecorresponding image data 36. As discussed above, image data 36 may bestored in image data memory 130.

Method 500 then proceeds to block 515 which involves generating a recordof the rock fall event. The record of the rock fall event may includerecordal of a number of parameters associated with the rock fall event.In particular embodiments, the block 515 record may include one or moreof: the event type (e.g. a block 395 or block 390 rock fall event); anumber of triggered sensors; a number N of block 410 sensors; start andend times of the event which may include the start time (t_(start)) forthe first triggered sensor and the end time (t_(end)) for the lastsensor to remain triggered; the estimated location of the hypocenter ofthe event (e.g. the block 445 hypocenter); the estimated PPV of theevent at the hypocenter (e.g. the value of the model parameter A_(i)determined in block 430 for the hypocenter selected in block 445); theestimated event energy (e.g. the block 275 energy); the PPV and theassociated time t_(PPV) for each triggered sensor; the maxima (andassociated times) of one or more other block 305 event specificparameters (e.g. STA/LTA_(max), E_(max) or the like); and any otherparameter of which data processor 120 may be aware. In some embodiments,the block 515 record may also include one or more of the block 510images of the estimated event location.

Method 500 then proceeds to block 520 which involves evaluation of analarm criteria. In one particular embodiment, the block 520 alarmcriteria may involve comparison to determine whether the estimated PPVof the event at the hypocenter (e.g. the value of the model parameterA_(i) determined in block 430 for the hypocenter selected in block 445)is greater than a PPV alarm threshold (thresh_PPV_alarm). In otherembodiments, the block 520 alarm criteria may involve comparison todetermine whether the estimated event energy (e.g. the block 275 energy)is greater than an energy alarm threshold (thresh_KE_alarm). It will beappreciated, based on equation (15) above, that in the above-describedembodiment, these two block 520 alarm criteria are equivalent andrelated by the experimentally determined scaling factor k. In someembodiments, the block 520 alarm criteria may be additionally oralternatively based on inquiries into one or more other parameter(s)measured or estimated by system 10.

If the block 520 inquiry is positive (e.g. the estimated PPV of theevent at the hypocenter is greater than a PPV alarm threshold(thresh_PPV_alarm)), then method 500 proceeds along the block 520 YESoutput to block 530 which may involve triggering an alarm and/ortransmitting the block 515 event record directly back to an offsitelocation (e.g. via network connection 28 to remote workstation 30). Theblock 530 alarm may involve triggering sensory stimulus at remoteworkstation 30 and/or an email at remote workstation 30 or the like. Insome embodiments, when the block 530 alarm is received at remoteworkstation 30, the block 515 event record (including any block 510images) may be evaluated by human personnel. If the event is determinedby human personnel to be worthy of service disruption, then vehiculartraffic may be prevented from traveling on track section 12 until theevent is investigated more thoroughly and/or cleared. In someembodiments, human intervention may not be desired or required and theblock 530 alarm may cause a communication to be directed to rail vehicleoperators to alert them to the event and to cause them to stop travelingon or toward track section 12.

If the block 520 inquiry is negative (e.g. the estimated PPV of theevent at the hypocenter is less than the PPV alarm threshold(thresh_PPV_alarm)), then method 500 proceeds along the block 520 NOoutput to block 540. In the illustrated embodiment, block 540 involvestransmitting and/or logging the event in the normal course (i.e. withouttriggering an alarm). Block 540 may involve storing the block 515 eventrecord in local memory (e.g. in data logger 110, memory 128 and/or imagedata memory 130) until such time as signal processing unit 26 is polledfor events (e.g. by remote workstation 30 over network connection 28).Depending on the availability of local memory, in other embodiments,block 540 may involve transmitting the block 515 event record (e.g. toremote workstation 30 over network connection 28) without triggering analarm.

FIG. 10 schematically illustrates a method 600 for deployment of system10 according to an example embodiment. Method 600 commences in block 610which involves assessing geotechnical characteristics of the environmentin the vicinity of track section 12. Block 610 may involve simulatingrock fall events using drops of known weights from known heights atknown locations (i.e. test drops). Block 610 may involve using portablesensor arrays (similar to sensor arrays 18) and portable signalprocessing units 26. Block 610 may involve assessing one or more of:

-   -   ambient noise characteristics (including, by way of non-limiting        example, characterizing noise from sources such as waterfalls,        running water sources, winds, nearby traffic and other sources        of surface noise);    -   the surface wave velocity in the substrate in a vicinity of        track section 12;    -   the soil quality factor (e.g. the parameter B_(o) described        above);    -   the accuracy range of block 270 location estimation method (e.g.        the inventors have determined that the accuracy of method 400        may decrease with distance between the sensors and the rock fall        location);    -   assessing an amount of data scattering resulting from acoustic        energy crossing an obstacle (e.g. the track in circumstances        where sensors are installed on both sides of track section 12 or        if track section 12 has curvature) and, if significant energy        loss takes place, then determining that data from “shadowed”        sensors should not be mixed with the remaining sensors;    -   verifying that gain settings associated with signal conditioning        circuitry 102 are suitable to capture events in a range of        interest; and/or    -   the like.

Method 600 then proceeds to block 620 which involves using the block 610information to determine a sensor density for system 10 and determiningthe associated system layout. Block 620 may involve comparing PPVsassociated with test drops at various distances against backgroundnoise. In some embodiments, the PPV of the smallest event necessary tobe detected should be greater than 3 times the background noise level.In other embodiments, this ratio is 4-5 times.

Method 600 then proceeds to block 630 which involves installing system10 in accordance with the block 620 layout. The portable sensor arraysand portable signal processing units may be replaced by permanent sensorarrays 18 and signal processing unit 26. Some of the geotechnicalparameters determined in block 610 may be reassessed using the permanentsystem components. Optional block 640 may involve determining abackground noise level (and an associated LTA constant c) which may beused, in some embodiments, to compute the modified STA/LTA ratio inaccordance with equation (3) described above.

Method 600 then proceeds to block 650 which involves testing system 10by running system 10 for a period of time sufficient to capture alltypes of detectable events. System 10 (and in particular software 124used by data processor 120) may be adjusted as needed during thistesting period to optimize performance. Digital sensor signalsassociated with particular events may be recorded so that they can beused again to evaluate changes to software 124. In block 660, system 10is commissioned to operate, but is subject to regular routine testingand recalibration as desired.

In some embodiments, it may be desirable to attempt to discriminateevents (and/or series of events) caused by human(s) and/or otheranimal(s) from events caused by rock fall, rail traffic and/or othersource of surface noise. FIG. 11 illustrated a method 700 which may beused to discriminate a series of events that may be caused by a human orother animal according to a particular embodiment. Method 700 may beperformed in method 200 (FIG. 7A) after the detection of an event. Forexample, method 700 may be performed between blocks 260 and 265 and/orbetween blocks 275 and 280.

Method 700 commences in block 710 which involves determining a temporalcorrelation between the current event and the previous M events. Theparameter M may be based on empirical evidence and may depend on sensorsensitivity, the importance of detection of animals in the vicinity oftrack section 12 or the like. The block 710 temporal correlation may bedetermined using a wide variety of techniques known to those skilled inthe art. One such technique involves determining a mean time of the lastM events (e.g. the mean start time t_(start) of the last M events) andcomparing the time that is furthest from the mean time with the meantime. In accordance with this technique, a large difference indicates afairly weak temporal correlation and a small difference indicates afairly strong temporal correlation. Another technique involves computingthe statistical standard deviation σ of the times of the last M events(e.g. the mean start times t_(start) of the last M events). Inaccordance with this technique, a large deviation indicates a relativelyweak temporal correlation, whereas a small deviation indicates arelatively strong temporal correlation.

If the block 710 inquiry indicates that the temporal correlation of thelast M events is less than a threshold (temp_corr_thresh), then method700 may proceed along the block 710 NO output to block 720 where method700 concludes that the series of events were not produced by an animal.If, on the other hand, the block 710 inquiry indicates that the temporalcorrelation of the last M events is greater than a threshold(temp_corr_thresh), then method 700 may proceed along the block 710 YESoutput to block 730. Block 730 involves evaluation of a spatialcorrelation of the last N events. In some embodiments, the block 710number of events M is equal to the block 730 number of events N. Theblock 730 spatial correlation may be determined on the basis of theevent locations determined in block 270 (e.g. method 400), for example.The block 730 spatial correlations may be determined using any of alarge variety of techniques known to those skilled in the art, includingthose described above for block 710.

If the block 730 inquiry indicates that the spatial correlation of thelast N events is less than a threshold (spat_corr_thresh), then method700 may proceed along the block 730 NO output to block 740 where method700 concludes that the series of events were not produced by an animal.If, on the other hand, the block 730 inquiry indicates that the spatialcorrelation of the last N events is greater than a threshold(spat_corr_thresh), then method 700 may proceed along the block 730 YESoutput to block 750, which involves concluding that the series of eventswas most likely caused by human(s) or other animal(s).

Where track section 12 is located in a region having relatively largeamounts of active train traffic and/or highrail vehicular traffic, thereis a relatively high likelihood of false positive events related to suchactive train and/or highrail vehicular traffic. In addition to trainsand highrail vehicles that are moving at regular speed through suchactive regions, such active regions may be associated with relativelylarge amounts of “cultural noise”. Such cultural noise may include, byway of non-limiting example, slow or stationary trains or highrailvehicles, movement of track maintenance personnel and/or equipment, siteexcavation and/or construction work and the associated movement ofpersonnel and/or equipment, right-of-way maintenance and/or the like. Insuch active regions (or in any other regions), it may be desirable toinclude additional sensors, additional processing techniques and/orother additional techniques to minimize (to the extent possible) thedetection of false positive events.

In some embodiments, system 10 may include particular vehicle detectionsensors for detecting slow moving and/or stationary trains, slow movingand/or stationary highrail vehicles and/or other vehicles operating in avicinity of such active track sections. Such vehicle detection sensorsmay include, for example, magnetometers which may be mounted directly totrack section 12 and/or at a distance (e.g. 0.5 m-2.5 m) away from tracksection 12 (e.g. in ballast 52), ultrasound vehicle sensors, optical(e.g. infrared) vehicle sensors and/or the like. Magnetometers may sensethe presence of iron or other magnetic materials associated with trainsand/or highrail vehicles. Ultrasound vehicle sensors may sense thepresence of trains and/or highrail vehicles using reflected acousticenergy. Optical vehicle sensors may sense the presence of trains and/orhighrail vehicles by sensing the interruption of an optical (e.g.infrared) beam.

Vehicle detection sensors which may be incorporated into system 10 todetect slow moving and/or stationary trains, slow moving and/orstationary highrail vehicles and/or other vehicles operating in avicinity of track section 12 may additionally or alternatively includemagnetic wheel detectors of the type used in the rail industry totrigger so-called “hot box” detectors. Such wheel detectors may bemounted directly to track section 12 (e.g. tracks 54 and/or ties 56). Astheir name implies, magnetic wheel detectors may be used to detect thewheels of trains and highrail vehicles.

A typical rail car may have a total of eight wheels (four on each side)with four (two on each side) near the front of the car and four (two oneach side) near the rear of the car. FIG. 12A schematically depicts atypical signal 800 emitted from a magnetic wheel detector in response tothe passage of a single car of a train over the wheel detector (aftersuitable amplification and optional temporal filtering (e.g. smoothingto reduce high frequency noise)). It can be seen from FIG. 12A thatsignal 800 exhibits four spikes A, B, C, D. Spikes A, B, C, D correspondto the detection of the four rail car wheels (on one side of the car) bythe wheel detector magnet. The first two spikes A, B of signal 800 areassociated with the two wheels near the front of the car and the secondtwo spikes C, D are associated with the two wheels near the rear of thecar (assuming that the car is moving forwardly).

The wheel detector signal for a highrail vehicle (having the same wheelpattern as the rail car associated with the FIG. 12A signal) may exhibita shape similar to that of signal 800, except that the time between thefirst two spikes and the second two spikes may be less because of therelatively short distance between the front wheels and rear wheels of ahighrail vehicle. For train cars or highrail vehicles with differentwheel patterns, signal 800 may have a different signature. However,there will typically be an identifiable signal feature (e.g. a spike orthe like) associated with each wheel (on one side) of the car/vehicle.FIG. 12B schematically shows a typical signal 802 emitted from amagnetic wheel detector in response to the passage of a train havingmultiple cars (after suitable amplification and optional temporalfiltering (e.g. smoothing to reduce high frequency noise)). It can beseen from signal 802 that a feature pattern or signature similar to thatof signal 800 (FIG. 12A) is repeated for each car in the train.

In some embodiments, system 10 may comprise one or more vehicledetection sensors (e.g. magnetometers, ultrasound vehicle sensors,optical vehicle sensors, wheel detectors and/or the like) which may beused to detect the presence of a train or a highrail vehicle on tracksection 12 and/or to discriminate whether a detected/triggered event isa train or highrail vehicle on track section 12. Such vehicle detectionsensors may be provided as part of one or more corresponding sensorarrays 18 and may provide corresponding vehicle detection information 22to signal processing unit 26 over transmission lines 24. Alternatively,such vehicle detection sensors could be provided independently of sensorarrays 18 and may independently communicate with signal processing unit26. Vehicle detection information received from vehicle detectionsensors may be handled by signal processing unit 26, DAU 116 and/or datalogger 110 in the same or similar manner as other sensor information 22discussed herein. Data from vehicle detection sensors may be loggedduring a time period when an event is triggered (e.g. between the timesof a block 225 YES output and a block 245 YES output (FIG. 7A)) or maybe logged continually. Once stored in data logger 110 or DAU 116,information from vehicle detection sensors can be processed by dataprocessor 120 to generate other forms of processing parameters 150. Someor all of these processing parameters 150 may be used in turn to detectthe presence of a train or a highrail vehicle on track section 12 and/orto discriminate whether a detected/triggered event is a train orhighrail vehicle on track section 12 (e.g. as a part of block 255 and/ormethod 300).

A number of exemplary embodiments incorporating wheel detector typevehicle sensors are now described. It will be appreciated that in manyinstances, other types of vehicle detection sensors could be used inaddition to, or as alternatives to, wheel detectors with suitablyappropriate modifications of the exemplary embodiments described herein.

Signals from one or more wheel detectors may be subjected to anamplitude thresholding criteria to detect the presence of a train or ahigh rail vehicle on track section 12. Referring to FIG. 12B, if it isdetermined that the absolute value of a wheel detector signal is greaterthan a threshold value wheel_thresh, then it may be concluded that thereis a train or highrail vehicle overtop of the wheel detector at thattime or that a triggered event was the result of a train or highrailvehicle. In some embodiments, system 10 may record or otherwise observea time associated with each instance that the wheel detector signalcrosses the threshold wheel_thresh. In some embodiments, system 10 mayrecord or otherwise observe only times associated with the absolutevalue of the wheel detector signal crossing the threshold wheel_threshin a particular direction (e.g. from below the threshold to above thethreshold or vice versa). In some embodiments, the value of wheel_threshmay be low or even zero, since a typical wheel detector is notparticularly noisy or susceptible to false positive events.

Such a thresholding criteria may be incorporated into the post eventprocessing of block 255 and/or method 300 described above. For example,if it is determined that the absolute value of a wheel detector signalwas greater than the threshold wheel_thresh at any time during the eventperiod (e.g. between t_(start) and t_(end)), then block 255/method 300may conclude that the event was a train or high rail vehicle. As anotherexample, if it is determined that the absolute value of a wheel detectorwas greater than the threshold wheel_thresh a number of times during theevent period (e.g. between t_(start) and t_(end)), but that this numberof times was less than a threshold number of times wheel_num_thresh,then block 255/method 300 may conclude that the event was a highrailvehicle as opposed to a train.

In addition to detecting the presence of a highrail vehicle or train,system 10 may use a plurality of wheel detectors spaced apart from oneanother by known distances to estimate the direction and/or speed of apassing vehicle. For example, where two wheel detectors are spaced apartfrom one another by a known distance D, the direction of travel of thepassing vehicle may be determined by detecting which wheel detectorsignal leads the other and an estimate of the speed of the train or thehighrail vehicle may be determined by dividing the wheel detectorseparation distance D by the temporal difference between correspondingfeatures of the wheel detector signals at the two wheel detectors. FIG.12C schematically depicts wheel detector signals 804A, 804B associatedwith the passage of a highrail vehicle over a pair of wheel detectorsand the manner in which temporal differences between correspondingfeatures of wheel detector signals 804A, 804B may be used to determinethe direction and speed of a vehicle. It can be seen from FIG. 12C, thatsignal 804A leads signal 804B. System 10 may therefore determine thatthe train or highrail vehicle that generated signals 804A, 804B wasmoving from the direction of the sensor associated with signal 804Atoward the direction of the sensor associated with signal 804B. FIG. 12Cexhibits a temporal difference Δt₁ between corresponding spikes 806A,806B of wheel detector signals 804A, 804B. The speed of a train orhighrail vehicle may be estimated to be

${v_{1} = {\frac{D}{\Delta \; t_{1}} = \frac{D}{\left( {t_{1B} - t_{1A}} \right)}}},$

where t_(1A) may be the time that signal 804A crosses the thresholdwheel_thresh at peak 806A and t_(1B) may be the time that signal 804Bcrosses the threshold wheel_thresh at peak 806B.

In addition to the first temporal difference Δt₁ between correspondingfirst spikes 806A, 806B of wheel detector signals 804A, 804B, FIG. 12Cexhibits the determination of a subsequent temporal difference Δt_(n)between corresponding subsequent spikes 808A, 808B of wheel detectorsignals 804A, 804B. System 10 may estimate the speed of a train orhighrail vehicle at a subsequent time associated with the n^(th)corresponding features of wheel detector signals 804A, 804B to be

${v_{n} = {\frac{D}{\Delta \; t_{n}} = \frac{D}{\left( {t_{nB} - t_{nA}} \right)}}},$

where t_(nA) may be the time that signal 804A crosses the thresholdwheel_thresh at peak 808A and t_(nB) may be the time that signal 804Bcrosses the threshold wheel_thresh at peak 808B.

In some embodiments, system 10, may estimate a speed for allcorresponding features of wheel detector signals 804A, 804B (e.g. everytime the absolute value of wheel detector signals 804A, 804B both crossthe threshold wheel_thresh), for all corresponding features of wheeldetector signals 804A, 804B when wheel detector signals 804A, 804B crossthe threshold wheel_thresh in a certain direction (e.g. every time theabsolute value of wheel detector signals 804A, 804B both cross frombelow, to above, the threshold wheel_thresh), or for every number ofcorresponding features of wheel detection signals 804A, 804B (e.g. everyk^(th) corresponding feature) of wheel detector signals 804A, 804B. Thenumber k of features between speed estimates can be a configurableparameter of system 10 and may depend on available processor resources.In some embodiments, data from wheel detectors may be logged andcorresponding speeds may be estimated only after an event has beentriggered (e.g. after the block 225 inquiry results in a YES output(FIG. 7A) or after t_(start)). The correspondence between features ofthe signals 804A, 804B associated with a pair of wheel detectors may bemaintained by suitable indices which may be incremented each time that athreshold crossing event (e.g. a wheel detection signal 804A, 804Bcrosses wheel_thresh) is recorded for that wheel detection signal 804A,804B.

In some embodiments, the estimated speeds v_(n) (or the determinedtemporal differences Δt_(n)) at different times may be processed (e.g.integrated or differentiated) to determine an estimated position and/oracceleration/deceleration of a train or highrail vehicle. Suchinformation may be used to estimate the location that a train orhighrail vehicle comes to rest on track section 12. Some systems mayincorporate more than two wheel detectors. In such systems, velocity,position and/or acceleration estimates based on different sensor pairscan be combined (e.g. averaged) to determine a better estimate of thevehicle velocity, position and/or acceleration. Also, velocitydifferences between different sensor pairs could be used as anothertechnique for estimating acceleration. For example, if sensor A isspaced apart from sensor B by a distance D and sensor B is spaced apartfrom sensor C by a distance D, and it is determined that the temporaldifference Δt_(AB) between the sensor A and B signals for a particularfeature is greater than the temporal difference Δt_(BC) between thesensor B and C signals for the same particular feature, then it may beconcluded that the vehicle is accelerating as it moves from sensor A toB to C.

In some embodiments, system 10 may detect the presence of trains and/orhighrail vehicles on track section 12 using the cross-correlation ofsignals from multiple sensors of the same type which are spaced apartfrom one another by known distances D. Sensors which may be used forthis cross-correlation process may include ballast sensors (e.g.acoustic ballast sensors 50 of the type described above), rail sensors(e.g. rail sensors 80 of the type described above), wheel detectorsand/or the like. Cross-correlation data may be generated for timeperiods when an event is triggered (e.g. between the times of a block225 YES output and a block 245 YES output (FIG. 7A)) or may be loggedcontinually. Once obtained, cross-correlation data can be processed bydata processor 120 to generate other forms of processing parameters 150.Some or all of these processing parameters 150 may be used in turn todetect the presence of a train or a highrail vehicle on track section 12and/or to discriminate whether a detected/triggered event is a train orhighrail vehicle on track section 12 (e.g. as a part of block 255 and/ormethod 300).

FIG. 13 exhibits a typical cross-correlation waveform 820 associatedwith the signals from a pair of spaced apart ballast sensors 50 when atrain is moving (or has moved) over track section 12 at a relativelyconstant speed (after suitable amplification and optional temporalfiltering (e.g. smoothing to reduce high frequency noise) of the signalsfrom sensors 50). It can be seen from FIG. 13, that cross-correlationwaveform 820 exhibits a reasonably sharp peak 822 at a time t_(CCmax).

In some embodiments, system 10 may determine the presence of a train ora highrail vehicle on track section 10 when peak 822 ofcross-correlation waveform 829 is greater than a threshold valueCC_thresh and when the absolute value of the time t_(CCmax) of peak 822occurs within a window between the times t₁ and t₂. This determinationmay be a part of block 255/method 300, although this is not necessary.The threshold CC_thresh may be experimentally determined and may be aconfigurable parameter of system 10. In some embodiments, the times t₁and t₂ can be configured to correspond to maximum and minimum expectedspeeds of a train or highrail vehicle. For example, if a train isexpected to move with a maximum speed v_(max) and it is known that thesensors associated with cross-correlation signal 820 as spaced from oneanother by a distance D, then t₁ may be set to

$t_{1} = \frac{D}{v_{\max}}$

and the train is expected to move with a minimum speed v_(min), then t₂may be set to

$t_{2} = {\frac{D}{v_{\min}}.}$

The times t₁, t₂ can also be experimentally determined and configuredparameters of system 10. The actual speed of a train or highrail vehiclemay be estimated from the time t_(CCmax) associated with the peak 822 ofcross-correlation signal 820 according to

$v = {\frac{D}{t_{CCmax}}.}$

This variation of this speed estimate over time may be processed (e.g.integrated or differentiated) to determine an estimated position and/oracceleration/deceleration of a train or highrail vehicle. Suchinformation may be used to estimate the location that a train orhighrail vehicle comes to rest on track section 12. The direction oftravel of a train or highrail vehicle can also be determined from thecross-correlation between two sensors, since the time t_(CCmax)associated with the cross-correlation peak 822 will be in the positiveor negative half axis depending on the direction of motion of the trainor highrail vehicle.

In some embodiments, cross-correlation analysis may be performed on aplurality of different sensor combinations (e.g. combinations involvingmore than two sensors) before concluding that an event is associatedwith a train or highrail vehicle (i.e. that an event is not a rockfall). For example, it may be required that each (or some percentage) ofthe cross-correlations of a plurality of different sensor combinationsexhibits a peak that is greater than a threshold CC_thresh and occurswithin a specified temporal window before concluding that an event is atrain or a highrail vehicle. The cross-correlation of two or moredifferent sensor combinations can also be used to estimate theacceleration of a vehicle. Consider again the example where sensor A isspaced apart from sensor B by a distance D and sensor B is spaced apartfrom sensor C by a distance D. If the time of the cross-correlation peak(t_(CCmax)) is greater for the cross-correlation of the signals fromsensors A and B than the time of the cross-correlation peak (t_(CCmax))for the cross-correlation of the signals from sensors B and C, then itcan be concluded that the train/vehicle is accelerating as it moves fromA to B to C. As another example, if the time of the cross-correlationpeak (t_(CCmax)) for the signals from sensors A and C is less than twicethe time of the cross-correlation peak (t_(CCmax)) for the signals fromsensors A and B, then it can be concluded that the train/vehicle isaccelerating as it moves from A to B to C.

In some embodiments, system 10 may include the ability for all or partof system 10 to be temporarily shut-off in some circumstances. Forexample, a signal may be sent to signal processing unit 26 via networkconnection 28 (or otherwise communicated to system 10) which causessystem 10 (e.g. signal processing unit 26) to temporarily disable (e.g.disregard information received from) one or more of sensor arrays 18.This temporary shut-off signal may be generated in any of a variety ofmanners. By way of non-limiting example, if track maintenance,right-of-way, excavation or construction personnel and/or equipment willbe working in a vicinity of a particular group of sensor arrays 18,then:

-   -   a shut-off signal for that group of sensor arrays 18 may be        communicated from a communication device (not shown) connected        to network connection 28 by such personnel (or by other suitable        personnel);    -   optional cameras 34 may be motion activated and may communicate        video signal(s) (e.g. via network connection 28) to a person who        may determine whether a group of sensors arrays 18 should be        temporarily shut-off;    -   a signal may be communicated to system 10 from a GPS-enabled        device carried by such personnel or coupled to such equipment        which may indicate the location of the personnel or equipment        and may thereby enable system 10 to determine which sensor        arrays 18 should be temporarily disabled; and/or    -   a signal from some other sensor or group of sensors (e.g. a        light-activated IR sensor, a radio frequency identification        (RFID) sensor and/or the like) may be communicated to system 10.        Such sensors may be strategically located to indicated to system        10 which sensor arrays 18 should be temporarily disabled.

In cases where system 10 is shut-off in whole or in part by way of amanually generated signal, then it may be desirable to have someautomated technique for re-activating system 10 to avoid such personnelaccidentally leaving system 10 in a shut-off state. By way ofnon-limiting example, such automated technique may include: a temporalre-activation (e.g. system 10 reactivates after a period (e.g. a userconfigurable period) of time; a sensor based reactivation (e.g. system10 reactivates when a light sensor determines that it is dark, where aGPS-enabled device determines that it is outside of a vicinity of tracksection 12 or the like); an automated reminder to a suitable person toreactivate system 10 (e.g. communicated over network 28); and/or thelike.

As will be apparent to those skilled in the art in the light of theforegoing disclosure, many alterations and modifications are possible inthe practice of this invention without departing from the spirit orscope thereof. For example:

-   -   The particular embodiments of the methods described above are        exemplary in nature. In other embodiments, portions of these        methods my be modified or changed. In some embodiments, aspects        of these methods may be performed in suitable orders other than        the orders described above. By way of non-limiting example, in        some embodiments of method 300 (FIG. 7B), the procedures of        blocks 360-390 may be performed before the procedures of blocks        310-330 and/or blocks 335-355 or the procedures of blocks        335-355 may be performed prior to the procedures of blocks        310-330. Those skilled in the art will appreciate that there are        other circumstances in which the order of particular operations        may be changed in circumstances where this is desirable.    -   Method 300 of the illustrated embodiment described above        involves discriminating a variety of different types of events        (i.e. train events, highrail vehicle events, surface noise        events, insignificant rock fall events and significant rock fall        events. This is not necessary. In some embodiments, it is        desirable to discriminate a smaller number of events (e.g. the        two categories of significant rock fall events and other        events). In such embodiments, method 300 may be suitable        modified such that the block 310 NO output, optional block 335        YES output, block 360 NO output and block 392 YES output all        lead to the same conclusion (i.e. other type of event) and        method 300 may conclude a significant rock fall event when the        block 392 inquiry is negative (i.e. block 392 NO output). In        other such embodiments, some of blocks 365-390 may be maintained        to discriminate small rock fall events or rock fall        accumulation.    -   In some embodiments, method 200 and/or method 300 may be        modified to provide an inquiry into a minimum delay between        events (Δevent). Events which occur at within a time (and/or        number of samples) separation less than the minimum delay Δevent        from one another may be determined to belong to the same event.        In particular embodiments, such closely spaced events may be        merged into a single event or one or more of such closely spaced        events may be ignored.    -   In some embodiments, method 200 and/or method 300 may be        modified to provide an inquiry into a minimum number of        triggered sensors (#_sensor_min). If the number of sensors        triggered by an even is less than this minimum number of sensors        (#_sensor_min), then the event can be determined to be too small        to be of concern.

FIG. 9B described above makes use of a parameter ΔT_(start) to determinethe sensors to be included in the block 410 group of sensors. Thisparameter Δt_(start) or a similar (possibly larger) temporal parametermay be used to determine a maximum arrival time difference. If a firstsensor is triggered at a time t_(start) _(—) _(sensor#1) and one or moresensors become triggered after this maximum arrival time difference,then the subsequently triggered sensors can be determined to belong to aseparate event. The maximum arrival time difference can be determinedbased at least in part of the experimentally determined surface wavevelocity in the substrate in a vicinity of track section 12.

-   -   Some of the above described embodiments describe using an        experimentally determined average B₀ for the model parameter        B_(i) of equation (7) and/or equation (15). In some embodiments,        this parameter B₀ may be the same for a particular track section        12, but may differ as between each of a plurality of modular        track sections 12 which may be incorporated into a overall        system or this parameter B₀ may vary locally within a track        section (12).    -   The methods described above involve the discrimination of a        number of events. These events represent non-limiting examples        of events that may be discriminated by system 10. In other        embodiments, system 10 may be configured to discriminate other        types of events, such as, by way of non-limiting example: switch        points being moved, locomotive bells/horns and thermal expansion        and the accompanying rail creep atop the ties caused by solar        heating of the rail.    -   In some embodiments, system 10 can exhibit one of two        states—rock fall and clear-to-pass. The rock fall state can        indicate that system 10 has detected an event that may be a rock        fall and consequently a train should not pass through track        section 12 without taking precautionary measures (e.g. slowing        to a speed at which braking may be effective, stopping and        waiting for a crew to arrive to investigate the event, stopping        and waiting for the state of system 10 to enter the        clear-to-pass state and/or the like. To be as safe as possible,        system 10 may default to the rock fall state. System 10 can        raise and alarm or take other suitable action when it determines        that its state should be changed to rock fall. In some        embodiments, system 10 can be reset from a rock fall state to a        clear-to-pass state if a train passes through track section 12        without incident. For example, if system 10 is in a rock fall        state, then a precautionary measure that could be taken is for a        train to slow to a speed where the train could be safely brought        to a stop after visually sighting a rock fall event. If,        however, the train is able to pass through the site of the        predicted rock fall event without incident, then the state of        system 10 may be reset to clear-to-pass.    -   In some embodiments (e.g. applications where the reliability of        system 10 is considered to be crucial), system 10 may be made        redundant through use of redundant components. For example,        referring to FIG. 1, system 10 may be modified to include        redundant sensors arrays 18 (e.g. each individual sensor array        18 shown in FIG. 1 would be replaced by a plurality of redundant        sensors arrays 18, if one sensor array 18 were to fail, system        10 could revert to its redundant backup sensor array). By way of        non-limiting example, system 10 could also comprise redundant        image capture devices 34, transmission lines 24, signal        processing units 26 and network connections 28.    -   Optional image capturing devices (e.g. cameras) 34 may be        remotely controlled by a user via network connection 28. In some        embodiments, upon detection of a rock fall event (or any other        event) by system 10, system 10 and/or a remote user may control        image capture devices 34 to capture one or more images of the        event location. Images captured by image capture devices 34 may        be communicated over network connection 28 to a control center,        where they may be reviewed by an operator. The operator may then        decide manually whether the event is a legitimate rock fall        event or whether the event is some other type of event.    -   The term acoustic is used throughout this description and the        accompanying claims. It will be appreciated that in the context        of this description and the accompanying claims, the term        acoustic should be understood to refer generally to mechanical        and/or vibrational energy which may travel through any medium.        Acoustic waves and acoustic sensors should be understood to        refer generally to waves which transfer this mechanical and/or        vibrational energy through any medium and sensors which detect        this mechanical and/or vibrational energy.

Accordingly, the scope of the invention should be determined inaccordance with the following claims.

What is claimed is:
 1. A system for detection of rock fall in a vicinityof a section of railway track, the system comprising: a plurality ofballast sensors spaced apart along the track section, each ballastsensor located in a ballast proximate to the track section but spacedapart from rails and ties associated with the track section and eachballast sensor sensitive to acoustic energy and configured to generate acorresponding ballast sensor signal in response to detecting acousticenergy; a signal processing unit operatively connected to receive theballast sensor signals from the plurality of ballast sensors, the signalprocessing unit configured to detect rock fall events in a vicinity ofthe track section based, at least in part, on the ballast sensorsignals.
 2. A system according to claim 1 wherein the signal processingunit is configured to detect a plurality of different types of eventscomprising rock fall events, train events wherein a train travels overthe track section and highrail vehicle events wherein a highrail vehicletravels over the track section and wherein the signal processing unit isconfigured to discriminate rock fall events from train events orhighrail vehicle events.
 3. A system according to claim 2 wherein thesignal processing unit is configured to detect an event for a particularone of the ballast sensors based, at least in part, on its correspondingballast sensor signal.
 4. A system according to claim 3 wherein thesignal processing unit is configured to detect a start of the event andan associated time t_(start), for the particular one of the ballastsensors, when a STA/LTA parameter associated with the correspondingballast sensor signal is greater than a start trigger threshold(thresh_start).
 5. A system according to claim wherein the signalprocessing unit is configured to detect an end of the event and anassociated time t_(end), for the particular one of the ballast sensors,when the STA/LTA parameter associated with the corresponding ballastsensor signal is less than an end trigger threshold (thresh_end).
 6. Asystem according to claim 4 wherein the signal processing unit isconfigured to determine the STA/LTA parameter for the correspondingballast sensor signal according to one of:${{(i)\mspace{14mu} \left( \frac{STA}{LTA} \right)_{n}} = {{\frac{\frac{\sum\limits_{i = {({n - {({a - 1})}})}}^{i = n}{x_{i}}}{a}}{\frac{\sum\limits_{i = {({n - {({b - 1})}})}}^{i = n}{x_{i}}}{b}}\mspace{14mu} {where}\mspace{14mu} b} > a > {0\mspace{14mu} {and}\mspace{14mu} n} \geq a}},b$where: x_(i) represents a value of an i^(th) sample of the correspondingballast sensor signal, n is an index of a current sample x_(n), a is anSTA duration constant, b is an LTA duration constant and$\left( \frac{STA}{LTA} \right)_{n}$ is the STA/LTA parameter; and${({ii})\mspace{14mu} \left( \frac{STA}{LTA} \right)_{{mod},n}} = {{\frac{\frac{\sum\limits_{i = {({n - {({a - 1})}})}}^{i = n}x_{i}}{a}}{c}\mspace{14mu} {where}\mspace{14mu} n} > a > 0}$where: x_(i) represents a value of an i^(th) sample of the correspondingballast sensor signal, n is an index of a current sample x_(n), a is anSTA duration constant, c is an experimentally determined constant thatis representative of an LTA during event free times and$\left( \frac{STA}{LTA} \right)_{{mod},n}$ is the STA/LTA parameter. 7.A system according to claim 3 wherein the signal processing unit isconfigured to detect a start of the event and an associated timet_(start), for the particular one of the ballast sensors, when an energyparameter associated with the corresponding ballast sensor signal isgreater than a start trigger threshold (E_thresh_start), the energyparameter comprising a windowed average of a squared amplitude of thecorresponding ballast sensor signal.
 8. A system according to claim 7wherein the signal processing unit is configured to detect an end of theevent and an associated time t_(end), for the particular one of theballast sensors, when the energy parameter associated with thecorresponding ballast sensor signal is less than an end triggerthreshold (E_thresh_end).
 9. A system according to claim 7 wherein thesignal processing unit is configured to determine the energy parameterfor the corresponding ballast sensor signal according to:$E_{n} = \frac{\sum\limits_{i = {n - {({d - 1})}}}^{n}\left( x_{i} \right)^{2}}{d}$where: x_(i) represents a value of an i^(th) sample of the correspondingballast sensor signal, n is an index of a current sample x_(n), d is awindow duration constant and E_(n) is the energy parameter.
 10. A systemaccording to claim 3 wherein the signal processing unit is configured todetermine a duration t_(dur) associated with the event for theparticular one of the ballast sensors based, at least in part, on itscorresponding ballast sensor signal.
 11. A system according to claim 10wherein the signal processing unit is configured to compare the eventduration t_(dur) with one or more duration criteria and to determine onthe basis of this comparison that the event is not a rock fall event.12. A system according to claim 11 wherein the signal processing unit isconfigured to determine that the event is a train event or a highrailvehicle event based at least in part on the comparison of the eventduration t_(dur) with the one or more duration criteria.
 13. A systemaccording to claim 12 wherein the signal processing unit is configuredto determine, for the particular one of the ballast sensors, a PPVparameter which represents a magnitude of a sample of the correspondingballast sensor signal with the largest absolute value during the eventand wherein the signal processing unit is configured to compare the PPVparameter with one or more magnitude criteria and to determine on thebasis of this comparison whether the event is a train event or ahighrail vehicle event.
 14. A system according to claim 3 wherein thesignal processing unit is configured to determine a spectral powerdistribution associated with the event for the particular one of theballast sensors based, at least in part, on its corresponding ballastsensor signal.
 15. A system according to claim 14 wherein the signalprocessing unit is configured to compare the spectral power distributionwith one or more spectral criteria and to determine on the basis of thiscomparison that the event is not a rock fall event.
 16. A systemaccording to claim 15 wherein the one or more spectral criteria comprisea frequency threshold (thresh_freq) and the signal processing unit isconfigured to determine that the event is a train event or a highrailvehicle event when the spectral power distribution comprises more than aparticular percentage of its power at frequencies above the frequencythreshold (thresh_freq).
 17. A system according to claim 16 wherein thesignal processing unit is configured to determine, for the particularone of the ballast sensors, a PPV parameter which represents a magnitudeof a sample of the corresponding ballast sensor signal with the largestabsolute value during the event and the signal processing unit isconfigured to compare the PPV parameter with one or more magnitudecriteria and to determine on the basis of this comparison whether theevent is a train event or a highrail vehicle event.
 18. A systemaccording to claim 3 comprising one or more rail sensors, each railsensor operatively contacting the rails or the ties associated with thetrack section and each rail sensor sensitive to acoustic energy andconfigured to generate a corresponding rail sensor signal in response todetecting acoustic energy and wherein the signal processing unit isoperatively connected to receive the rail sensor signal.
 19. A systemaccording to claim 18 wherein the signal processing unit is configuredto determine a rail sensor spectral power distribution associated withthe event for a particular one of the rail sensors based, at least inpart, on its corresponding rail sensor signal and configured to comparethe rail sensor spectral power distribution with one or more spectralcriteria and to determine on the basis of this comparison that the eventis not a rock fall event.
 20. A system according to claim 3 wherein thesignal processing unit is configured to determine a PPV parameterassociated with the event for the particular one of the ballast sensorsand its corresponding ballast sensor signal, the PPV parameterrepresenting a magnitude of a sample of the corresponding ballast sensorsignal with the largest absolute value during the event.